Overview

Dataset statistics

Number of variables23
Number of observations11935
Missing cells68691
Missing cells (%)25.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory184.0 B

Variable types

Numeric9
Categorical12
Unsupported2

Warnings

PostTypeId has constant value "1" Constant
CreationDate has a high cardinality: 11934 distinct values High cardinality
Body has a high cardinality: 11935 distinct values High cardinality
OwnerDisplayName has a high cardinality: 1713 distinct values High cardinality
LastEditorDisplayName has a high cardinality: 907 distinct values High cardinality
LastEditDate has a high cardinality: 10984 distinct values High cardinality
LastActivityDate has a high cardinality: 11933 distinct values High cardinality
Title has a high cardinality: 11935 distinct values High cardinality
Tags has a high cardinality: 10083 distinct values High cardinality
ClosedDate has a high cardinality: 1369 distinct values High cardinality
CommunityOwnedDate has a high cardinality: 556 distinct values High cardinality
ContentLicense is highly correlated with PostTypeIdHigh correlation
PostTypeId is highly correlated with ContentLicenseHigh correlation
AcceptedAnswerId has 1421 (11.9%) missing values Missing
ParentId has 11935 (100.0%) missing values Missing
DeletionDate has 11935 (100.0%) missing values Missing
OwnerUserId has 306 (2.6%) missing values Missing
OwnerDisplayName has 9539 (79.9%) missing values Missing
LastEditorUserId has 776 (6.5%) missing values Missing
LastEditorDisplayName has 10220 (85.6%) missing values Missing
LastEditDate has 620 (5.2%) missing values Missing
ClosedDate has 10560 (88.5%) missing values Missing
CommunityOwnedDate has 11379 (95.3%) missing values Missing
CreationDate is uniformly distributed Uniform
Body is uniformly distributed Uniform
OwnerDisplayName is uniformly distributed Uniform
LastActivityDate is uniformly distributed Uniform
Title is uniformly distributed Uniform
ClosedDate is uniformly distributed Uniform
CommunityOwnedDate is uniformly distributed Uniform
Id has unique values Unique
Body has unique values Unique
Title has unique values Unique
ParentId is an unsupported type, check if it needs cleaning or further analysis Unsupported
DeletionDate is an unsupported type, check if it needs cleaning or further analysis Unsupported
CommentCount has 2501 (21.0%) zeros Zeros

Reproduction

Analysis started2021-05-21 10:06:52.424256
Analysis finished2021-05-21 10:07:18.551828
Duration26.13 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

Id
Real number (ℝ≥0)

UNIQUE

Distinct11935
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7945389.421
Minimum9
Maximum63607158
Zeros0
Zeros (%)0.0%
Memory size93.4 KiB
2021-05-21T12:07:18.715390image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile111760
Q1987685
median3964681
Q311182984
95-th percentile29959399.7
Maximum63607158
Range63607149
Interquartile range (IQR)10195299

Descriptive statistics

Standard deviation9851468.285
Coefficient of variation (CV)1.239897476
Kurtosis3.408015073
Mean7945389.421
Median Absolute Deviation (MAD)3516410
Skewness1.840408439
Sum9.482822274 × 1010
Variance9.705142737 × 1013
MonotocityNot monotonic
2021-05-21T12:07:18.904203image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17203201
 
< 0.1%
32262821
 
< 0.1%
118329141
 
< 0.1%
14608921
 
< 0.1%
59050541
 
< 0.1%
60312141
 
< 0.1%
300243531
 
< 0.1%
306694741
 
< 0.1%
48401021
 
< 0.1%
219256711
 
< 0.1%
Other values (11925)11925
99.9%
ValueCountFrequency (%)
91
< 0.1%
111
< 0.1%
131
< 0.1%
191
< 0.1%
421
< 0.1%
ValueCountFrequency (%)
636071581
< 0.1%
590066021
< 0.1%
584415141
< 0.1%
574561881
< 0.1%
568393071
< 0.1%

PostTypeId
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size93.4 KiB
1
11935 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11935
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
111935
100.0%
2021-05-21T12:07:19.229999image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-21T12:07:19.350754image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
111935
100.0%

Most occurring characters

ValueCountFrequency (%)
111935
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11935
100.0%

Most frequent character per category

ValueCountFrequency (%)
111935
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11935
100.0%

Most frequent character per script

ValueCountFrequency (%)
111935
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11935
100.0%

Most frequent character per block

ValueCountFrequency (%)
111935
100.0%

AcceptedAnswerId
Real number (ℝ≥0)

MISSING

Distinct10514
Distinct (%)100.0%
Missing1421
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean9170627.091
Minimum77
Maximum63955114
Zeros0
Zeros (%)0.0%
Memory size93.4 KiB
2021-05-21T12:07:19.463670image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile135206.95
Q11221941.75
median4737625
Q313198476.25
95-th percentile33163627.2
Maximum63955114
Range63955037
Interquartile range (IQR)11976534.5

Descriptive statistics

Standard deviation10980995.05
Coefficient of variation (CV)1.197409397
Kurtosis3.021260069
Mean9170627.091
Median Absolute Deviation (MAD)4217358.5
Skewness1.755893514
Sum9.641997324 × 1010
Variance1.205822523 × 1014
MonotocityNot monotonic
2021-05-21T12:07:19.644250image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49552971
 
< 0.1%
4866631
 
< 0.1%
79755111
 
< 0.1%
330641
 
< 0.1%
25873981
 
< 0.1%
12822651
 
< 0.1%
27065371
 
< 0.1%
92555071
 
< 0.1%
107754421
 
< 0.1%
62936261
 
< 0.1%
Other values (10504)10504
88.0%
(Missing)1421
 
11.9%
ValueCountFrequency (%)
771
< 0.1%
2981
< 0.1%
3621
< 0.1%
3961
< 0.1%
5161
< 0.1%
ValueCountFrequency (%)
639551141
< 0.1%
625517861
< 0.1%
624384641
< 0.1%
621963401
< 0.1%
618595611
< 0.1%

ParentId
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing11935
Missing (%)100.0%
Memory size93.4 KiB

CreationDate
Categorical

HIGH CARDINALITY
UNIFORM

Distinct11934
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size93.4 KiB
2008-09-01 18:16:41
 
2
2008-10-13 21:29:50
 
1
2008-10-14 18:07:37
 
1
2010-04-29 01:41:54
 
1
2010-11-24 17:51:13
 
1
Other values (11929)
11929 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters226765
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11933 ?
Unique (%)> 99.9%

Sample

1st row2008-09-03 06:30:31
2nd row2011-11-29 17:30:29
3rd row2008-09-03 20:34:33
4th row2008-09-03 21:32:02
5th row2013-05-20 07:53:02
ValueCountFrequency (%)
2008-09-01 18:16:412
 
< 0.1%
2008-10-13 21:29:501
 
< 0.1%
2008-10-14 18:07:371
 
< 0.1%
2010-04-29 01:41:541
 
< 0.1%
2010-11-24 17:51:131
 
< 0.1%
2009-06-30 12:21:401
 
< 0.1%
2012-06-04 21:26:401
 
< 0.1%
2014-07-08 00:46:511
 
< 0.1%
2010-01-19 04:55:031
 
< 0.1%
2012-07-23 20:35:021
 
< 0.1%
Other values (11924)11924
99.9%
2021-05-21T12:07:20.073300image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2008-09-1650
 
0.2%
2008-09-1744
 
0.2%
2008-09-1837
 
0.2%
2008-09-1937
 
0.2%
2008-09-2536
 
0.2%
2008-09-1532
 
0.1%
2008-09-2429
 
0.1%
2008-09-2329
 
0.1%
2008-10-0227
 
0.1%
2008-09-2626
 
0.1%
Other values (13835)23523
98.5%

Most occurring characters

ValueCountFrequency (%)
044833
19.8%
133446
14.7%
229962
13.2%
-23870
10.5%
:23870
10.5%
11935
 
5.3%
311602
 
5.1%
49963
 
4.4%
59801
 
4.3%
98550
 
3.8%
Other values (3)18933
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number167090
73.7%
Dash Punctuation23870
 
10.5%
Other Punctuation23870
 
10.5%
Space Separator11935
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
044833
26.8%
133446
20.0%
229962
17.9%
311602
 
6.9%
49963
 
6.0%
59801
 
5.9%
98550
 
5.1%
87573
 
4.5%
65785
 
3.5%
75575
 
3.3%
ValueCountFrequency (%)
-23870
100.0%
ValueCountFrequency (%)
11935
100.0%
ValueCountFrequency (%)
:23870
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common226765
100.0%

Most frequent character per script

ValueCountFrequency (%)
044833
19.8%
133446
14.7%
229962
13.2%
-23870
10.5%
:23870
10.5%
11935
 
5.3%
311602
 
5.1%
49963
 
4.4%
59801
 
4.3%
98550
 
3.8%
Other values (3)18933
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII226765
100.0%

Most frequent character per block

ValueCountFrequency (%)
044833
19.8%
133446
14.7%
229962
13.2%
-23870
10.5%
:23870
10.5%
11935
 
5.3%
311602
 
5.1%
49963
 
4.4%
59801
 
4.3%
98550
 
3.8%
Other values (3)18933
8.3%

DeletionDate
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing11935
Missing (%)100.0%
Memory size93.4 KiB

Score
Real number (ℝ)

Distinct1893
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean559.3235861
Minimum-146
Maximum25400
Zeros0
Zeros (%)0.0%
Memory size93.4 KiB
2021-05-21T12:07:20.250747image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-146
5-th percentile119
Q1226
median364
Q3604
95-th percentile1627.6
Maximum25400
Range25546
Interquartile range (IQR)378

Descriptive statistics

Standard deviation770.9057898
Coefficient of variation (CV)1.378282284
Kurtosis197.8182413
Mean559.3235861
Median Absolute Deviation (MAD)167
Skewness9.709142639
Sum6675527
Variance594295.7367
MonotocityNot monotonic
2021-05-21T12:07:20.458209image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26437
 
0.3%
27134
 
0.3%
18934
 
0.3%
28033
 
0.3%
20333
 
0.3%
20932
 
0.3%
23631
 
0.3%
21731
 
0.3%
20731
 
0.3%
22031
 
0.3%
Other values (1883)11608
97.3%
ValueCountFrequency (%)
-1461
< 0.1%
121
< 0.1%
281
< 0.1%
291
< 0.1%
322
< 0.1%
ValueCountFrequency (%)
254001
< 0.1%
224841
< 0.1%
178461
< 0.1%
124521
< 0.1%
110511
< 0.1%

ViewCount
Real number (ℝ≥0)

Distinct11844
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450605.762
Minimum4448
Maximum9426474
Zeros0
Zeros (%)0.0%
Memory size93.4 KiB
2021-05-21T12:07:20.650444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum4448
5-th percentile47727.4
Q1139462.5
median281931
Q3549259
95-th percentile1416149.5
Maximum9426474
Range9422026
Interquartile range (IQR)409796.5

Descriptive statistics

Standard deviation554720.3136
Coefficient of variation (CV)1.231054639
Kurtosis33.94365368
Mean450605.762
Median Absolute Deviation (MAD)171456
Skewness4.349079131
Sum5377979769
Variance3.077146263 × 1011
MonotocityNot monotonic
2021-05-21T12:07:20.857624image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1915112
 
< 0.1%
834582
 
< 0.1%
1063232
 
< 0.1%
1636552
 
< 0.1%
1748902
 
< 0.1%
1354332
 
< 0.1%
1416572
 
< 0.1%
606792
 
< 0.1%
5117602
 
< 0.1%
750382
 
< 0.1%
Other values (11834)11915
99.8%
ValueCountFrequency (%)
44481
< 0.1%
59691
< 0.1%
69741
< 0.1%
71401
< 0.1%
74601
< 0.1%
ValueCountFrequency (%)
94264741
< 0.1%
87307031
< 0.1%
80011671
< 0.1%
75697051
< 0.1%
71810951
< 0.1%

Body
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct11935
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size93.4 KiB
<p>I'm trying to perform some offline maintenance (dev database restore from live backup) on my dev database, but the 'Take Offline' command via SQL Server Management Studio is performing <strong>extremely</strong> slowly - on the order of 30 minutes plus now. I am just about at my wits end and I can't seem to find any references online as to what might be causing the speed problem, or how to fix it.</p> <p>Some sites have suggested that open connections to the database cause this slowdown, but the only application that uses this database is my dev machine's IIS instance, and the service is stopped - there are no more open connections.</p> <p>What could be causing this slowdown, and what can I do to speed it up?</p>
 
1
<p><a href="https://stackoverflow.com/questions/292357/whats-the-difference-between-git-pull-and-git-fetch">Another question</a> says that <code>git pull</code> is like a <code>git fetch</code> + <code>git merge</code>.</p> <p>But what is the difference between <code>git pull</code> and <code>git fetch</code> + <code>git rebase</code>?</p>
 
1
<p>Sealed classes are described in 'Programming in Scala', but sealed traits are not. Where can I find more information about a sealed trait?</p> <p>I would like to know, if a sealed trait is the same as a sealed class? Or, if not, what are the differences? When is it a good idea to use a sealed trait (and when not)?</p>
 
1
<p>It is supposed to be generally preferable to use a <code>StringBuilder</code> for string concatenation in Java. Is this always the case?</p> <p>What I mean is this: Is the overhead of creating a <code>StringBuilder</code> object, calling the <code>append()</code> method and finally <code>toString()</code> already smaller then concatenating existing strings with the <code>+</code> operator for two strings, or is it only advisable for more (than two) strings?</p> <p>If there is such a threshold, what does it depend on (perhaps the string length, but in which way)?</p> <p>And finally, would you trade the readability and conciseness of the <code>+</code> concatenation for the performance of the <code>StringBuilder</code> in smaller cases like two, three or four strings?</p> <p>Explicit use of <code>StringBuilder</code> for regular concatenations is being mentioned as obsolete at <a href="https://stackoverflow.com/questions/4019180/obsolete-java-optimization-tips">obsolete Java optimization tips</a> as well as at <a href="https://stackoverflow.com/questions/2248278/java-urban-myths">Java urban myths</a>.</p>
 
1
<p>In terms of Java, when someone asks: </p> <blockquote> <p>what is polymorphism?</p> </blockquote> <p>Would <strong>overloading</strong> or <strong>overriding</strong> be an acceptable answer?</p> <p>I think there is a bit more to it than that. </p> <p><em>IF you had a abstract base class that defined a method with no implementation, and you defined that method in the sub class, is that still overridding?</em></p> <p>I think <strong>overloading</strong> is not the right answer for sure.</p>
 
1
Other values (11930)
11930 

Length

Max length24750
Median length512
Mean length829.1379137
Min length23

Characters and Unicode

Total characters9895761
Distinct characters408
Distinct categories22 ?
Distinct scripts11 ?
Distinct blocks23 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11935 ?
Unique (%)100.0%

Sample

1st row<p>I found an example in the <a href="http://msdn2.microsoft.com/en-us/bb330936.aspx" rel="noreferrer">VS2008 Examples</a> for Dynamic LINQ that allows you to use a sql-like string (e.g. <code>OrderBy("Name, Age DESC"))</code> for ordering. Unfortunately, the method included only works on <code>IQueryable&lt;T&gt;</code>. Is there any way to get this functionality on <code>IEnumerable&lt;T&gt;</code>?</p>
2nd row<p>RequireJS seems to do something internally that caches required javascript files. If I make a change to one of the required files, I have to rename the file in order for the changes to be applied. </p> <p>The common trick of appending a version number as a querystring param to the end of the filename does not work with requirejs <code>&lt;script src="jsfile.js?v2"&gt;&lt;/script&gt;</code> </p> <p>What I am looking for is a way to prevent this internal cacheing of RequireJS required scripts without having to rename my script files every time they are updated.</p> <p><strong>Cross-Platform Solution:</strong></p> <p>I am now using <code>urlArgs: "bust=" + (new Date()).getTime()</code> for automatic cache-busting during development and <code>urlArgs: "bust=v2"</code> for production where I increment the hard-coded version num after rolling out an updated required script.</p> <p><strong>Note:</strong></p> <p>@Dustin Getz mentioned in a recent answer that Chrome Developer Tools will drop breakpoints during debugging when Javascript files are continuously refreshed like this. One workaround is to write <code>debugger;</code> in code to trigger a breakpoint in most Javascript debuggers.</p> <p><strong>Server-Specific Solutions:</strong></p> <p>For specific solutions that may work better for your server environment such as Node or Apache, see some of the answers below.</p>
3rd row<p>Inspired by <a href="https://devblogs.microsoft.com/oldnewthing/20080902-00/?p=21003" rel="noreferrer">Raymond Chen's post</a>, say you have a 4x4 two dimensional array, write a function that rotates it 90 degrees. Raymond links to a solution in pseudo code, but I'd like to see some real world stuff.</p> <pre><code>[1][2][3][4] [5][6][7][8] [9][0][1][2] [3][4][5][6] </code></pre> <p>Becomes:</p> <pre><code>[3][9][5][1] [4][0][6][2] [5][1][7][3] [6][2][8][4] </code></pre> <p><strong>Update</strong>: Nick's answer is the most straightforward, but is there a way to do it better than n^2? What if the matrix was 10000x10000?</p>
4th row<p>What is the best way to get <code>IDENTITY</code> of inserted row?</p> <p>I know about <code>@@IDENTITY</code> and <code>IDENT_CURRENT</code> and <code>SCOPE_IDENTITY</code> but don't understand the pros and cons attached to each.</p> <p>Can someone please explain the differences and when I should be using each?</p>
5th row<p>I am trying to synchronise a project that I have on in my Android Studio folder to GitHub, but I am not fully sure what to do other than adding my credentials in the options menu. Could someone give me a quick guide, please?</p>
ValueCountFrequency (%)
<p>I'm trying to perform some offline maintenance (dev database restore from live backup) on my dev database, but the 'Take Offline' command via SQL Server Management Studio is performing <strong>extremely</strong> slowly - on the order of 30 minutes plus now. I am just about at my wits end and I can't seem to find any references online as to what might be causing the speed problem, or how to fix it.</p> <p>Some sites have suggested that open connections to the database cause this slowdown, but the only application that uses this database is my dev machine's IIS instance, and the service is stopped - there are no more open connections.</p> <p>What could be causing this slowdown, and what can I do to speed it up?</p> 1
 
< 0.1%
<p><a href="https://stackoverflow.com/questions/292357/whats-the-difference-between-git-pull-and-git-fetch">Another question</a> says that <code>git pull</code> is like a <code>git fetch</code> + <code>git merge</code>.</p> <p>But what is the difference between <code>git pull</code> and <code>git fetch</code> + <code>git rebase</code>?</p> 1
 
< 0.1%
<p>Sealed classes are described in 'Programming in Scala', but sealed traits are not. Where can I find more information about a sealed trait?</p> <p>I would like to know, if a sealed trait is the same as a sealed class? Or, if not, what are the differences? When is it a good idea to use a sealed trait (and when not)?</p> 1
 
< 0.1%
<p>It is supposed to be generally preferable to use a <code>StringBuilder</code> for string concatenation in Java. Is this always the case?</p> <p>What I mean is this: Is the overhead of creating a <code>StringBuilder</code> object, calling the <code>append()</code> method and finally <code>toString()</code> already smaller then concatenating existing strings with the <code>+</code> operator for two strings, or is it only advisable for more (than two) strings?</p> <p>If there is such a threshold, what does it depend on (perhaps the string length, but in which way)?</p> <p>And finally, would you trade the readability and conciseness of the <code>+</code> concatenation for the performance of the <code>StringBuilder</code> in smaller cases like two, three or four strings?</p> <p>Explicit use of <code>StringBuilder</code> for regular concatenations is being mentioned as obsolete at <a href="https://stackoverflow.com/questions/4019180/obsolete-java-optimization-tips">obsolete Java optimization tips</a> as well as at <a href="https://stackoverflow.com/questions/2248278/java-urban-myths">Java urban myths</a>.</p> 1
 
< 0.1%
<p>In terms of Java, when someone asks: </p> <blockquote> <p>what is polymorphism?</p> </blockquote> <p>Would <strong>overloading</strong> or <strong>overriding</strong> be an acceptable answer?</p> <p>I think there is a bit more to it than that. </p> <p><em>IF you had a abstract base class that defined a method with no implementation, and you defined that method in the sub class, is that still overridding?</em></p> <p>I think <strong>overloading</strong> is not the right answer for sure.</p> 1
 
< 0.1%
<p>Once upon a time, we can watch the most popular repositories (Most forked or Most watched) at this page (<a href="https://github.com/popular/watched">https://github.com/popular/watched</a>) of Github. like this:</p> <p><img src="https://i.imgur.com/nkEzfwY.png" alt="img"></p> <p>But now when you try to explore repos, you can only see the Top 25 trending repositories. like this: <a href="https://github.com/trending">https://github.com/trending</a></p> <p>Why Github change this, and is there any way to find out the list of the most popular repos?</p> 1
 
< 0.1%
<p>Is there a destructor for Java? I don't seem to be able to find any documentation on this. If there isn't, how can I achieve the same effect?</p> <p>To make my question more specific, I am writing an application that deals with data and the specification say that there should be a 'reset' button that brings the application back to its original just launched state. However, all data have to be 'live' unless the application is closed or reset button is pressed.</p> <p>Being usually a C/C++ programmer, I thought this would be trivial to implement. (And hence I planned to implement it last.) I structured my program such that all the 'reset-able' objects would be in the same class so that I can just destroy all 'live' objects when a reset button is pressed.</p> <p>I was thinking if all I did was just to dereference the data and wait for the garbage collector to collect them, wouldn't there be a memory leak if my user repeatedly entered data and pressed the reset button? I was also thinking since Java is quite mature as a language, there should be a way to prevent this from happening or gracefully tackle this.</p> 1
 
< 0.1%
<p>I would like to know if there is an alternative to iFrames with HTML5. I mean by that, be able to inject cross-domains HTML inside of a webpage without using an iFrame.</p> 1
 
< 0.1%
<p>I am learning about Big O Notation running times and amortized times. I understand the notion of <em>O(n)</em> linear time, meaning that the size of the input affects the growth of the algorithm proportionally...and the same goes for, for example, quadratic time <em>O(n<sup>2</sup>)</em> etc..even algorithms, such as permutation generators, with <em>O(n!)</em> times, that grow by factorials.</p> <p>For example, the following function is <em>O(n)</em> because the algorithm grows in proportion to its input <em>n</em>:</p> <pre><code>f(int n) { int i; for (i = 0; i &lt; n; ++i) printf("%d", i); } </code></pre> <p>Similarly, if there was a nested loop, the time would be O(n<sup>2</sup>).</p> <p>But what exactly is <em>O(log n)</em>? For example, what does it mean to say that the height of a complete binary tree is <em>O(log n)</em>?</p> <p>I do know (maybe not in great detail) what Logarithm is, in the sense that: log<sub>10</sub> 100 = 2, but I cannot understand how to identify a function with a logarithmic time.</p> 1
 
< 0.1%
<p>After I did brew update and brew upgrade, my postgres got some problem. I tried to uninstall postgres and install again, but it didn't work as well. </p> <p>This is the error message.(I also got this error message when I try to do rake db:migrate)</p> <pre><code>$ psql psql: could not connect to server: No such file or directory Is the server running locally and accepting connections on Unix domain socket "/tmp/.s.PGSQL.5432"? </code></pre> <p>How can I solve it?</p> <p>Mac version: Mountain lion.</p> <p>homebrew version: 0.9.3</p> <p>postgres version: psql (PostgreSQL) 9.2.1</p> <p>And this is what I did.</p> <pre><code>12:30 ~/D/works$ brew uninstall postgresql Uninstalling /usr/local/Cellar/postgresql/9.2.1... 12:31 ~/D/works$ brew uninstall postgresql Uninstalling /usr/local/Cellar/postgresql/9.1.4... 12:31 ~/D/works$ psql --version bash: /usr/local/bin/psql: No such file or directory 12:33 ~/D/works$ brew install postgresql ==&gt; Downloading http://ftp.postgresql.org/pub/source/v9.2.1/postgresql-9.2.1.tar.bz2 Already downloaded: /Library/Caches/Homebrew/postgresql-9.2.1.tar.bz2 ...... ...... ==&gt; Summary /usr/local/Cellar/postgresql/9.2.1: 2814 files, 38M, built in 2.7 minutes 12:37 ~/D/works$ initdb /usr/local/var/postgres -E utf8 The files belonging to this database system will be owned by user "laigary". This user must also own the server process. The database cluster will be initialized with locale "en_US.UTF-8". The default text search configuration will be set to "english". initdb: directory "/usr/local/var/postgres" exists but is not empty If you want to create a new database system, either remove or empty the directory "/usr/local/var/postgres" or run initdb with an argument other than "/usr/local/var/postgres". 12:39 ~/D/works$ mkdir -p ~/Library/LaunchAgents 12:39 ~/D/works$ cp /usr/local/Cellar/postgresql/9.2.1/homebrew.mxcl.postgresql.plist ~/Library/LaunchAgents/ 12:39 ~/D/works$ launchctl load -w ~/Library/LaunchAgents/homebrew.mxcl.postgresql.plist homebrew.mxcl.postgresql: Already loaded 12:39 ~/D/works$ pg_ctl -D /usr/local/var/postgres -l /usr/local/var/postgres/server.log start server starting 12:39 ~/D/works$ env ARCHFLAGS="-arch x86_64" gem install pg Building native extensions. This could take a while... Successfully installed pg-0.14.1 1 gem installed 12:42 ~/D/works$ psql --version psql (PostgreSQL) 9.2.1 12:42 ~/D/works$ psql psql: could not connect to server: No such file or directory Is the server running locally and accepting connections on Unix domain socket "/tmp/.s.PGSQL.5432"? </code></pre> <p>Now, after I reinstalled howbrew,when I use <code>$ psql</code>, It doesn't show any error message.</p> <p>But I run <code>rake db:migrate</code> in my rails app, it shows:</p> <pre><code>could not connect to server: No such file or directory Is the server running locally and accepting connections on Unix domain socket "/var/pgsql_socket/.s.PGSQL.5432"? /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/postgresql_adapter.rb:1213:in `initialize' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/postgresql_adapter.rb:1213:in `new' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/postgresql_adapter.rb:1213:in `connect' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/postgresql_adapter.rb:329:in `initialize' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/postgresql_adapter.rb:28:in `new' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/postgresql_adapter.rb:28:in `postgresql_connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:309:in `new_connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:319:in `checkout_new_connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:241:in `block (2 levels) in checkout' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:236:in `loop' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:236:in `block in checkout' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/monitor.rb:211:in `mon_synchronize' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:233:in `checkout' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:96:in `block in connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/monitor.rb:211:in `mon_synchronize' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:95:in `connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:404:in `retrieve_connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_specification.rb:170:in `retrieve_connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_specification.rb:144:in `connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/railties/databases.rake:107:in `rescue in create_database' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/railties/databases.rake:51:in `create_database' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/railties/databases.rake:40:in `block (3 levels) in &lt;top (required)&gt;' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/railties/databases.rake:40:in `each' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/railties/databases.rake:40:in `block (2 levels) in &lt;top (required)&gt;' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/task.rb:205:in `call' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/task.rb:205:in `block in execute' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/task.rb:200:in `each' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/task.rb:200:in `execute' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/task.rb:158:in `block in invoke_with_call_chain' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/monitor.rb:211:in `mon_synchronize' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/task.rb:151:in `invoke_with_call_chain' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/task.rb:144:in `invoke' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:116:in `invoke_task' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:94:in `block (2 levels) in top_level' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:94:in `each' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:94:in `block in top_level' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:133:in `standard_exception_handling' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:88:in `top_level' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:66:in `block in run' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:133:in `standard_exception_handling' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:63:in `run' /usr/local/bin/rake:32:in `&lt;main&gt;' Couldn't create database for {"adapter"=&gt;"postgresql", "encoding"=&gt;"unicode", "database"=&gt;"riy_development", "pool"=&gt;5, "username"=&gt;nil, "password"=&gt;nil} </code></pre> <p><strong>Finally I find the solution.</strong></p> <pre><code>$ sudo mkdir /var/pgsql_socket/ $ sudo ln -s /private/tmp/.s.PGSQL.5432 /var/pgsql_socket/ </code></pre> <p>This solution is little tricky, but it works. Hope anyone have a better solution</p> <p><strong>Update</strong></p> <p>This works for me as well.</p> <pre><code>rm /usr/local/var/postgres/postmaster.pid </code></pre> 1
 
< 0.1%
Other values (11925)11925
99.9%
2021-05-21T12:07:21.321704image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the49308
 
3.9%
40181
 
3.2%
to36545
 
2.9%
a33430
 
2.7%
i26570
 
2.1%
and19267
 
1.5%
is18081
 
1.4%
in16547
 
1.3%
of16077
 
1.3%
that11930
 
0.9%
Other values (116595)992056
78.7%

Most occurring characters

ValueCountFrequency (%)
1407644
 
14.2%
e809309
 
8.2%
t620460
 
6.3%
o538281
 
5.4%
a467639
 
4.7%
i433918
 
4.4%
n420878
 
4.3%
r414914
 
4.2%
s389979
 
3.9%
l288910
 
2.9%
Other values (398)4103829
41.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6546881
66.2%
Space Separator1408105
 
14.2%
Other Punctuation589974
 
6.0%
Math Symbol472711
 
4.8%
Uppercase Letter358863
 
3.6%
Control184345
 
1.9%
Decimal Number152567
 
1.5%
Close Punctuation52665
 
0.5%
Open Punctuation52594
 
0.5%
Dash Punctuation50969
 
0.5%
Other values (12)26087
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
͗6
 
2.3%
̮6
 
2.3%
͋6
 
2.3%
͇6
 
2.3%
̤5
 
1.9%
̀5
 
1.9%
ͤ5
 
1.9%
̗5
 
1.9%
̱5
 
1.9%
̊5
 
1.9%
Other values (89)210
79.5%
ValueCountFrequency (%)
e809309
12.4%
t620460
 
9.5%
o538281
 
8.2%
a467639
 
7.1%
i433918
 
6.6%
n420878
 
6.4%
r414914
 
6.3%
s389979
 
6.0%
l288910
 
4.4%
d275514
 
4.2%
Other values (80)1887079
28.8%
ValueCountFrequency (%)
I64134
17.9%
S29306
 
8.2%
T25311
 
7.1%
A21882
 
6.1%
C21545
 
6.0%
E16948
 
4.7%
P15871
 
4.4%
L14334
 
4.0%
R13847
 
3.9%
M13683
 
3.8%
Other values (36)122002
34.0%
ValueCountFrequency (%)
6
 
4.9%
6
 
4.9%
6
 
4.9%
6
 
4.9%
6
 
4.9%
6
 
4.9%
6
 
4.9%
6
 
4.9%
6
 
4.9%
6
 
4.9%
Other values (30)63
51.2%
ValueCountFrequency (%)
261
34.6%
120
15.9%
78
 
10.3%
56
 
7.4%
55
 
7.3%
¦48
 
6.4%
24
 
3.2%
👩20
 
2.6%
12
 
1.6%
👧10
 
1.3%
Other values (27)71
 
9.4%
ValueCountFrequency (%)
/178398
30.2%
.112134
19.0%
"59611
 
10.1%
,52014
 
8.8%
;44558
 
7.6%
:44120
 
7.5%
'33220
 
5.6%
&27253
 
4.6%
?19027
 
3.2%
#5115
 
0.9%
Other values (14)14524
 
2.5%
ValueCountFrequency (%)
>213954
45.3%
<213663
45.2%
=36502
 
7.7%
+5854
 
1.2%
|2257
 
0.5%
~433
 
0.1%
21
 
< 0.1%
×13
 
< 0.1%
3
 
< 0.1%
2
 
< 0.1%
Other values (7)9
 
< 0.1%
ValueCountFrequency (%)
030561
20.0%
127061
17.7%
222106
14.5%
314376
9.4%
411854
 
7.8%
510855
 
7.1%
69768
 
6.4%
99070
 
5.9%
88936
 
5.9%
77980
 
5.2%
ValueCountFrequency (%)
1407644
> 99.9%
 371
 
< 0.1%
28
 
< 0.1%
20
 
< 0.1%
20
 
< 0.1%
20
 
< 0.1%
2
 
< 0.1%
ValueCountFrequency (%)
(37405
71.1%
{8678
 
16.5%
[6509
 
12.4%
1
 
< 0.1%
1
 
< 0.1%
ValueCountFrequency (%)
`963
74.0%
^331
 
25.4%
´7
 
0.5%
˜1
 
0.1%
ValueCountFrequency (%)
$3797
99.7%
£5
 
0.1%
¢4
 
0.1%
¤1
 
< 0.1%
ValueCountFrequency (%)
²6
60.0%
³2
 
20.0%
1
 
10.0%
¹1
 
10.0%
ValueCountFrequency (%)
-50818
99.7%
82
 
0.2%
69
 
0.1%
ValueCountFrequency (%)
)37582
71.4%
}8623
 
16.4%
]6460
 
12.3%
ValueCountFrequency (%)
182080
98.8%
2238
 
1.2%
27
 
< 0.1%
ValueCountFrequency (%)
124
71.3%
48
 
27.6%
«2
 
1.1%
ValueCountFrequency (%)
296
68.4%
135
31.2%
»2
 
0.5%
ValueCountFrequency (%)
57
68.7%
25
30.1%
1
 
1.2%
ValueCountFrequency (%)
_19132
100.0%
ValueCountFrequency (%)
҉3
100.0%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6905600
69.8%
Common2989609
30.2%
Inherited289
 
< 0.1%
Cyrillic124
 
< 0.1%
Han65
 
< 0.1%
Hangul39
 
< 0.1%
Greek16
 
< 0.1%
Hebrew7
 
< 0.1%
Kannada6
 
< 0.1%
Hiragana4
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
1407644
47.1%
>213954
 
7.2%
<213663
 
7.1%
182080
 
6.1%
/178398
 
6.0%
.112134
 
3.8%
"59611
 
2.0%
,52014
 
1.7%
-50818
 
1.7%
;44558
 
1.5%
Other values (122)474735
 
15.9%
ValueCountFrequency (%)
25
 
8.7%
͗6
 
2.1%
̮6
 
2.1%
͋6
 
2.1%
͇6
 
2.1%
̤5
 
1.7%
̀5
 
1.7%
ͤ5
 
1.7%
̗5
 
1.7%
̱5
 
1.7%
Other values (90)215
74.4%
ValueCountFrequency (%)
e809309
 
11.7%
t620460
 
9.0%
o538281
 
7.8%
a467639
 
6.8%
i433918
 
6.3%
n420878
 
6.1%
r414914
 
6.0%
s389979
 
5.6%
l288910
 
4.2%
d275514
 
4.0%
Other values (86)2245798
32.5%
ValueCountFrequency (%)
о17
13.7%
а12
 
9.7%
к10
 
8.1%
н10
 
8.1%
е9
 
7.3%
т8
 
6.5%
и7
 
5.6%
с5
 
4.0%
Г4
 
3.2%
р4
 
3.2%
Other values (22)38
30.6%
ValueCountFrequency (%)
6
9.2%
6
9.2%
6
9.2%
6
9.2%
6
9.2%
6
9.2%
6
9.2%
6
9.2%
6
9.2%
3
 
4.6%
Other values (4)8
12.3%
ValueCountFrequency (%)
3
 
7.7%
3
 
7.7%
3
 
7.7%
3
 
7.7%
3
 
7.7%
3
 
7.7%
3
 
7.7%
3
 
7.7%
3
 
7.7%
3
 
7.7%
Other values (3)9
23.1%
ValueCountFrequency (%)
Θ3
18.8%
π2
12.5%
ε2
12.5%
ί2
12.5%
ν2
12.5%
α2
12.5%
ι2
12.5%
Ω1
 
6.2%
ValueCountFrequency (%)
ב1
14.3%
ר1
14.3%
י1
14.3%
צ1
14.3%
ק1
14.3%
ל1
14.3%
ה1
14.3%
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
ValueCountFrequency (%)
6
100.0%
ValueCountFrequency (%)
ܫ2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9892819
> 99.9%
Punctuation1018
 
< 0.1%
None700
 
< 0.1%
Box Drawing607
 
< 0.1%
Diacriticals264
 
< 0.1%
Cyrillic124
 
< 0.1%
CJK65
 
< 0.1%
Hangul39
 
< 0.1%
Arrows30
 
< 0.1%
Misc Symbols25
 
< 0.1%
Other values (13)70
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
1407644
 
14.2%
e809309
 
8.2%
t620460
 
6.3%
o538281
 
5.4%
a467639
 
4.7%
i433918
 
4.4%
n420878
 
4.3%
r414914
 
4.2%
s389979
 
3.9%
l288910
 
2.9%
Other values (88)4100887
41.5%
ValueCountFrequency (%)
296
29.1%
135
13.3%
124
12.2%
82
 
8.1%
70
 
6.9%
69
 
6.8%
57
 
5.6%
48
 
4.7%
28
 
2.8%
25
 
2.5%
Other values (10)84
 
8.3%
ValueCountFrequency (%)
 371
53.0%
¦48
 
6.9%
ö21
 
3.0%
👩20
 
2.9%
ä19
 
2.7%
å18
 
2.6%
×13
 
1.9%
é13
 
1.9%
👧10
 
1.4%
👦10
 
1.4%
Other values (68)157
22.4%
ValueCountFrequency (%)
2
40.0%
2
40.0%
1
20.0%
ValueCountFrequency (%)
261
43.0%
120
19.8%
78
 
12.9%
56
 
9.2%
55
 
9.1%
24
 
4.0%
5
 
0.8%
2
 
0.3%
2
 
0.3%
2
 
0.3%
ValueCountFrequency (%)
6
9.2%
6
9.2%
6
9.2%
6
9.2%
6
9.2%
6
9.2%
6
9.2%
6
9.2%
6
9.2%
3
 
4.6%
Other values (4)8
12.3%
ValueCountFrequency (%)
🟡1
50.0%
🟢1
50.0%
ValueCountFrequency (%)
9
100.0%
ValueCountFrequency (%)
21
70.0%
3
 
10.0%
2
 
6.7%
2
 
6.7%
1
 
3.3%
1
 
3.3%
ValueCountFrequency (%)
2
100.0%
ValueCountFrequency (%)
͗6
 
2.3%
̮6
 
2.3%
͋6
 
2.3%
͇6
 
2.3%
̤5
 
1.9%
̀5
 
1.9%
ͤ5
 
1.9%
̗5
 
1.9%
̱5
 
1.9%
̊5
 
1.9%
Other values (89)210
79.5%
ValueCountFrequency (%)
о17
13.7%
а12
 
9.7%
к10
 
8.1%
н10
 
8.1%
е9
 
7.3%
т8
 
6.5%
и7
 
5.6%
с5
 
4.0%
Г4
 
3.2%
р4
 
3.2%
Other values (22)38
30.6%
ValueCountFrequency (%)
2
25.0%
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
ValueCountFrequency (%)
3
100.0%
ValueCountFrequency (%)
3
 
7.7%
3
 
7.7%
3
 
7.7%
3
 
7.7%
3
 
7.7%
3
 
7.7%
3
 
7.7%
3
 
7.7%
3
 
7.7%
3
 
7.7%
Other values (3)9
23.1%
ValueCountFrequency (%)
12
48.0%
4
 
16.0%
2
 
8.0%
2
 
8.0%
2
 
8.0%
2
 
8.0%
1
 
4.0%
ValueCountFrequency (%)
6
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
ܫ2
100.0%
ValueCountFrequency (%)
˜1
100.0%
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
ValueCountFrequency (%)
ב1
14.3%
ר1
14.3%
י1
14.3%
צ1
14.3%
ק1
14.3%
ל1
14.3%
ה1
14.3%
ValueCountFrequency (%)
17
100.0%

OwnerUserId
Real number (ℝ≥0)

MISSING

Distinct9365
Distinct (%)80.5%
Missing306
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean629360.9859
Minimum1
Maximum11407978
Zeros0
Zeros (%)0.0%
Memory size93.4 KiB
2021-05-21T12:07:21.554084image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2187.4
Q149887
median234322
Q3759568
95-th percentile2629844.2
Maximum11407978
Range11407977
Interquartile range (IQR)709681

Descriptive statistics

Standard deviation1039230.318
Coefficient of variation (CV)1.651246807
Kurtosis17.64724265
Mean629360.9859
Median Absolute Deviation (MAD)218174
Skewness3.53420404
Sum7318838905
Variance1.079999654 × 1012
MonotocityNot monotonic
2021-05-21T12:07:21.755540image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6305118
 
0.2%
4915315
 
0.1%
17973614
 
0.1%
3967713
 
0.1%
487213
 
0.1%
995113
 
0.1%
488312
 
0.1%
138465212
 
0.1%
14337812
 
0.1%
1505511
 
0.1%
Other values (9355)11496
96.3%
(Missing)306
 
2.6%
ValueCountFrequency (%)
16
0.1%
31
 
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
114079781
< 0.1%
110728411
< 0.1%
108926911
< 0.1%
103395891
< 0.1%
101337971
< 0.1%

OwnerDisplayName
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct1713
Distinct (%)71.5%
Missing9539
Missing (%)79.9%
Memory size93.4 KiB
Ray Vega
 
12
Dan
 
11
Tom
 
10
Tim
 
10
anon
 
9
Other values (1708)
2344 

Length

Max length25
Median length8
Mean length8.379799666
Min length1

Characters and Unicode

Total characters20078
Distinct characters80
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1352 ?
Unique (%)56.4%

Sample

1st rowJohn Sheehan
2nd rowswilliams
3rd rowOded
4th rowMatt Sheppard
5th rowPatrick McElhaney
ValueCountFrequency (%)
Ray Vega12
 
0.1%
Dan11
 
0.1%
Tom10
 
0.1%
Tim10
 
0.1%
anon9
 
0.1%
J. Pablo Fern&#225;ndez9
 
0.1%
Claudiu9
 
0.1%
Simucal9
 
0.1%
Matt9
 
0.1%
Bill the Lizard8
 
0.1%
Other values (1703)2300
 
19.3%
(Missing)9539
79.9%
2021-05-21T12:07:22.187645image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
matt27
 
0.9%
brian24
 
0.8%
john22
 
0.7%
mark20
 
0.6%
chris19
 
0.6%
adam18
 
0.6%
dan18
 
0.6%
j16
 
0.5%
david16
 
0.5%
paul15
 
0.5%
Other values (1871)2977
93.9%

Most occurring characters

ValueCountFrequency (%)
a1711
 
8.5%
e1696
 
8.4%
r1313
 
6.5%
n1261
 
6.3%
o1192
 
5.9%
i1134
 
5.6%
s847
 
4.2%
780
 
3.9%
l779
 
3.9%
t764
 
3.8%
Other values (70)8601
42.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15602
77.7%
Uppercase Letter2647
 
13.2%
Decimal Number892
 
4.4%
Space Separator780
 
3.9%
Other Punctuation124
 
0.6%
Dash Punctuation20
 
0.1%
Connector Punctuation13
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
a1711
 
11.0%
e1696
 
10.9%
r1313
 
8.4%
n1261
 
8.1%
o1192
 
7.6%
i1134
 
7.3%
s847
 
5.4%
l779
 
5.0%
t764
 
4.9%
u550
 
3.5%
Other values (26)4355
27.9%
ValueCountFrequency (%)
S243
 
9.2%
M240
 
9.1%
J202
 
7.6%
B186
 
7.0%
D177
 
6.7%
A174
 
6.6%
C171
 
6.5%
G135
 
5.1%
T133
 
5.0%
R126
 
4.8%
Other values (17)860
32.5%
ValueCountFrequency (%)
2144
16.1%
1118
13.2%
3105
11.8%
098
11.0%
489
10.0%
973
8.2%
870
7.8%
569
7.7%
668
7.6%
758
6.5%
ValueCountFrequency (%)
.61
49.2%
&21
 
16.9%
#21
 
16.9%
;21
 
16.9%
ValueCountFrequency (%)
780
100.0%
ValueCountFrequency (%)
_13
100.0%
ValueCountFrequency (%)
-20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18249
90.9%
Common1829
 
9.1%

Most frequent character per script

ValueCountFrequency (%)
a1711
 
9.4%
e1696
 
9.3%
r1313
 
7.2%
n1261
 
6.9%
o1192
 
6.5%
i1134
 
6.2%
s847
 
4.6%
l779
 
4.3%
t764
 
4.2%
u550
 
3.0%
Other values (53)7002
38.4%
ValueCountFrequency (%)
780
42.6%
2144
 
7.9%
1118
 
6.5%
3105
 
5.7%
098
 
5.4%
489
 
4.9%
973
 
4.0%
870
 
3.8%
569
 
3.8%
668
 
3.7%
Other values (7)215
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII20065
99.9%
None8
 
< 0.1%
IPA Ext5
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
a1711
 
8.5%
e1696
 
8.5%
r1313
 
6.5%
n1261
 
6.3%
o1192
 
5.9%
i1134
 
5.7%
s847
 
4.2%
780
 
3.9%
l779
 
3.9%
t764
 
3.8%
Other values (59)8588
42.8%
ValueCountFrequency (%)
Š2
25.0%
ǝ2
25.0%
š1
12.5%
ź1
12.5%
ń1
12.5%
ı1
12.5%
ValueCountFrequency (%)
ɐ1
20.0%
ɯ1
20.0%
ʃ1
20.0%
ʞ1
20.0%
ɔ1
20.0%

LastEditorUserId
Real number (ℝ)

MISSING

Distinct4744
Distinct (%)42.5%
Missing776
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean2053495.456
Minimum-1
Maximum15054795
Zeros0
Zeros (%)0.0%
Memory size93.4 KiB
2021-05-21T12:07:22.449945image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1135729.5
median875915
Q33064538
95-th percentile7906188
Maximum15054795
Range15054796
Interquartile range (IQR)2928808.5

Descriptive statistics

Standard deviation2685047.38
Coefficient of variation (CV)1.307549706
Kurtosis3.209228415
Mean2053495.456
Median Absolute Deviation (MAD)820128
Skewness1.825662309
Sum2.29149558 × 1010
Variance7.209479434 × 1012
MonotocityNot monotonic
2021-05-21T12:07:22.712244image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63550755
 
6.3%
-1591
 
5.0%
6862601107
 
0.9%
1033581106
 
0.9%
355230104
 
0.9%
86009990
 
0.8%
80620289
 
0.7%
392411874
 
0.6%
275640959
 
0.5%
388537654
 
0.5%
Other values (4734)9130
76.5%
(Missing)776
 
6.5%
ValueCountFrequency (%)
-1591
5.0%
11
 
< 0.1%
31
 
< 0.1%
231
 
< 0.1%
291
 
< 0.1%
ValueCountFrequency (%)
150547952
< 0.1%
149797601
< 0.1%
148689971
< 0.1%
148370941
< 0.1%
146383961
< 0.1%

LastEditorDisplayName
Categorical

HIGH CARDINALITY
MISSING

Distinct907
Distinct (%)52.9%
Missing10220
Missing (%)85.6%
Memory size93.4 KiB
user456814
 
75
Roger Pate
 
29
user212218
 
25
Rich B
 
22
user166390
 
20
Other values (902)
1544 

Length

Max length25
Median length10
Mean length9.232069971
Min length2

Characters and Unicode

Total characters15833
Distinct characters82
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique650 ?
Unique (%)37.9%

Sample

1st rowJohn Sheehan
2nd rowJakub Šturc
3rd rowΤΖΩΤΖΙΟΥ
4th rowChris Fournier
5th rowcynicalman
ValueCountFrequency (%)
user45681475
 
0.6%
Roger Pate29
 
0.2%
user21221825
 
0.2%
Rich B22
 
0.2%
user16639020
 
0.2%
J.F. Sebastian17
 
0.1%
Chris Hanson17
 
0.1%
user626986413
 
0.1%
Ned Batchelder12
 
0.1%
user122812
 
0.1%
Other values (897)1473
 
12.3%
(Missing)10220
85.6%
2021-05-21T12:07:23.136245image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
user45681475
 
3.1%
chris43
 
1.8%
b30
 
1.2%
roger29
 
1.2%
pate29
 
1.2%
user21221825
 
1.0%
brian24
 
1.0%
rich23
 
1.0%
john22
 
0.9%
mark21
 
0.9%
Other values (1062)2093
86.7%

Most occurring characters

ValueCountFrequency (%)
e1353
 
8.5%
r1133
 
7.2%
a1057
 
6.7%
s803
 
5.1%
n801
 
5.1%
o783
 
4.9%
i734
 
4.6%
703
 
4.4%
u556
 
3.5%
t515
 
3.3%
Other values (72)7395
46.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10953
69.2%
Decimal Number2191
 
13.8%
Uppercase Letter1882
 
11.9%
Space Separator703
 
4.4%
Other Punctuation99
 
0.6%
Dash Punctuation3
 
< 0.1%
Connector Punctuation2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
B180
 
9.6%
J156
 
8.3%
M154
 
8.2%
R143
 
7.6%
S142
 
7.5%
C139
 
7.4%
A110
 
5.8%
D108
 
5.7%
G84
 
4.5%
P84
 
4.5%
Other values (23)582
30.9%
ValueCountFrequency (%)
e1353
12.4%
r1133
10.3%
a1057
 
9.7%
s803
 
7.3%
n801
 
7.3%
o783
 
7.1%
i734
 
6.7%
u556
 
5.1%
t515
 
4.7%
l501
 
4.6%
Other values (22)2717
24.8%
ValueCountFrequency (%)
1319
14.6%
6303
13.8%
4278
12.7%
8255
11.6%
5251
11.5%
2222
10.1%
3174
7.9%
9167
7.6%
7118
 
5.4%
0104
 
4.7%
ValueCountFrequency (%)
.69
69.7%
&10
 
10.1%
#10
 
10.1%
;10
 
10.1%
ValueCountFrequency (%)
703
100.0%
ValueCountFrequency (%)
-3
100.0%
ValueCountFrequency (%)
_2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12795
80.8%
Common2998
 
18.9%
Greek40
 
0.3%

Most frequent character per script

ValueCountFrequency (%)
e1353
 
10.6%
r1133
 
8.9%
a1057
 
8.3%
s803
 
6.3%
n801
 
6.3%
o783
 
6.1%
i734
 
5.7%
u556
 
4.3%
t515
 
4.0%
l501
 
3.9%
Other values (49)4559
35.6%
ValueCountFrequency (%)
703
23.4%
1319
10.6%
6303
10.1%
4278
 
9.3%
8255
 
8.5%
5251
 
8.4%
2222
 
7.4%
3174
 
5.8%
9167
 
5.6%
7118
 
3.9%
Other values (7)208
 
6.9%
ValueCountFrequency (%)
Τ10
25.0%
Ζ10
25.0%
Ω5
12.5%
Ι5
12.5%
Ο5
12.5%
Υ5
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII15784
99.7%
None47
 
0.3%
IPA Ext2
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
e1353
 
8.6%
r1133
 
7.2%
a1057
 
6.7%
s803
 
5.1%
n801
 
5.1%
o783
 
5.0%
i734
 
4.7%
703
 
4.5%
u556
 
3.5%
t515
 
3.3%
Other values (59)7346
46.5%
ValueCountFrequency (%)
Τ10
21.3%
Ζ10
21.3%
Ω5
10.6%
Ι5
10.6%
Ο5
10.6%
Υ5
10.6%
Š3
 
6.4%
ź1
 
2.1%
ń1
 
2.1%
ć1
 
2.1%
ValueCountFrequency (%)
ʞ1
50.0%
ɔ1
50.0%

LastEditDate
Categorical

HIGH CARDINALITY
MISSING

Distinct10984
Distinct (%)97.1%
Missing620
Missing (%)5.2%
Memory size93.4 KiB
2020-06-20 09:12:55
 
106
2017-05-23 12:10:41
 
9
2017-05-23 12:34:45
 
8
2017-05-23 12:26:23
 
7
2017-05-23 12:18:22
 
7
Other values (10979)
11178 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters214985
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10871 ?
Unique (%)96.1%

Sample

1st row2020-05-06 08:43:38
2nd row2013-12-11 17:09:20
3rd row2020-02-22 17:25:33
4th row2018-03-26 08:14:43
5th row2016-09-16 04:18:58
ValueCountFrequency (%)
2020-06-20 09:12:55106
 
0.9%
2017-05-23 12:10:419
 
0.1%
2017-05-23 12:34:458
 
0.1%
2017-05-23 12:26:237
 
0.1%
2017-05-23 12:18:227
 
0.1%
2017-05-23 12:26:356
 
0.1%
2017-05-23 11:55:106
 
0.1%
2017-05-23 11:47:286
 
0.1%
2017-05-23 10:31:385
 
< 0.1%
2017-05-23 11:33:265
 
< 0.1%
Other values (10974)11150
93.4%
(Missing)620
 
5.2%
2021-05-21T12:07:23.678108image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-05-23437
 
1.9%
2020-06-20108
 
0.5%
09:12:55106
 
0.5%
2020-04-1948
 
0.2%
2020-04-1738
 
0.2%
2017-03-2221
 
0.1%
2021-04-0118
 
0.1%
2019-02-2716
 
0.1%
2017-09-1015
 
0.1%
2018-11-0214
 
0.1%
Other values (13738)21809
96.4%

Most occurring characters

ValueCountFrequency (%)
037934
17.6%
132772
15.2%
230177
14.0%
-22630
10.5%
:22630
10.5%
11315
 
5.3%
310761
 
5.0%
510299
 
4.8%
49740
 
4.5%
97131
 
3.3%
Other values (3)19596
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number158410
73.7%
Dash Punctuation22630
 
10.5%
Other Punctuation22630
 
10.5%
Space Separator11315
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
037934
23.9%
132772
20.7%
230177
19.0%
310761
 
6.8%
510299
 
6.5%
49740
 
6.1%
97131
 
4.5%
76913
 
4.4%
86639
 
4.2%
66044
 
3.8%
ValueCountFrequency (%)
-22630
100.0%
ValueCountFrequency (%)
11315
100.0%
ValueCountFrequency (%)
:22630
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common214985
100.0%

Most frequent character per script

ValueCountFrequency (%)
037934
17.6%
132772
15.2%
230177
14.0%
-22630
10.5%
:22630
10.5%
11315
 
5.3%
310761
 
5.0%
510299
 
4.8%
49740
 
4.5%
97131
 
3.3%
Other values (3)19596
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII214985
100.0%

Most frequent character per block

ValueCountFrequency (%)
037934
17.6%
132772
15.2%
230177
14.0%
-22630
10.5%
:22630
10.5%
11315
 
5.3%
310761
 
5.0%
510299
 
4.8%
49740
 
4.5%
97131
 
3.3%
Other values (3)19596
9.1%

LastActivityDate
Categorical

HIGH CARDINALITY
UNIFORM

Distinct11933
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size93.4 KiB
2020-01-31 06:45:01
 
2
2020-01-31 06:44:36
 
2
2020-02-13 17:07:39
 
1
2020-10-06 02:01:45
 
1
2021-03-24 06:23:06
 
1
Other values (11928)
11928 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters226765
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11931 ?
Unique (%)> 99.9%

Sample

1st row2020-09-27 11:39:24
2nd row2018-09-12 07:43:01
3rd row2020-12-16 18:32:49
4th row2021-01-14 02:35:41
5th row2021-02-28 21:59:41
ValueCountFrequency (%)
2020-01-31 06:45:012
 
< 0.1%
2020-01-31 06:44:362
 
< 0.1%
2020-02-13 17:07:391
 
< 0.1%
2020-10-06 02:01:451
 
< 0.1%
2021-03-24 06:23:061
 
< 0.1%
2020-03-29 11:57:161
 
< 0.1%
2021-04-06 20:57:321
 
< 0.1%
2021-01-20 23:39:581
 
< 0.1%
2020-05-29 04:56:281
 
< 0.1%
2020-11-24 06:44:161
 
< 0.1%
Other values (11923)11923
99.9%
2021-05-21T12:07:24.057283image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-04-0784
 
0.4%
2021-04-0881
 
0.3%
2021-04-0671
 
0.3%
2021-04-0966
 
0.3%
2021-03-2465
 
0.3%
2021-03-2564
 
0.3%
2021-03-3163
 
0.3%
2021-03-2360
 
0.3%
2021-04-0259
 
0.2%
2021-04-0159
 
0.2%
Other values (13041)23198
97.2%

Most occurring characters

ValueCountFrequency (%)
043300
19.1%
237278
16.4%
131616
13.9%
-23870
10.5%
:23870
10.5%
11935
 
5.3%
311326
 
5.0%
49793
 
4.3%
59284
 
4.1%
96990
 
3.1%
Other values (3)17503
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number167090
73.7%
Dash Punctuation23870
 
10.5%
Other Punctuation23870
 
10.5%
Space Separator11935
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
043300
25.9%
237278
22.3%
131616
18.9%
311326
 
6.8%
49793
 
5.9%
59284
 
5.6%
96990
 
4.2%
86186
 
3.7%
75783
 
3.5%
65534
 
3.3%
ValueCountFrequency (%)
-23870
100.0%
ValueCountFrequency (%)
11935
100.0%
ValueCountFrequency (%)
:23870
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common226765
100.0%

Most frequent character per script

ValueCountFrequency (%)
043300
19.1%
237278
16.4%
131616
13.9%
-23870
10.5%
:23870
10.5%
11935
 
5.3%
311326
 
5.0%
49793
 
4.3%
59284
 
4.1%
96990
 
3.1%
Other values (3)17503
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII226765
100.0%

Most frequent character per block

ValueCountFrequency (%)
043300
19.1%
237278
16.4%
131616
13.9%
-23870
10.5%
:23870
10.5%
11935
 
5.3%
311326
 
5.0%
49793
 
4.3%
59284
 
4.1%
96990
 
3.1%
Other values (3)17503
7.7%

Title
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct11935
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size93.4 KiB
How can I use Ruby to colorize the text output to a terminal?
 
1
How do I redirect in expressjs while passing some context?
 
1
Get MD5 hash of big files in Python
 
1
How to Free Inode Usage?
 
1
How can I stop .gitignore from appearing in the list of untracked files?
 
1
Other values (11930)
11930 

Length

Max length149
Median length48
Mean length50.38868873
Min length13

Characters and Unicode

Total characters601389
Distinct characters116
Distinct categories17 ?
Distinct scripts6 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11935 ?
Unique (%)100.0%

Sample

1st rowDynamic LINQ OrderBy on IEnumerable<T> / IQueryable<T>
2nd rowPrevent RequireJS from Caching Required Scripts
3rd rowHow do you rotate a two dimensional array?
4th rowBest way to get identity of inserted row?
5th rowHow do you synchronise projects to GitHub with Android Studio?
ValueCountFrequency (%)
How can I use Ruby to colorize the text output to a terminal?1
 
< 0.1%
How do I redirect in expressjs while passing some context?1
 
< 0.1%
Get MD5 hash of big files in Python1
 
< 0.1%
How to Free Inode Usage?1
 
< 0.1%
How can I stop .gitignore from appearing in the list of untracked files?1
 
< 0.1%
Call a "local" function within module.exports from another function in module.exports?1
 
< 0.1%
Get String in YYYYMMDD format from JS date object?1
 
< 0.1%
jQuery Ajax calls and the Html.AntiForgeryToken()1
 
< 0.1%
Add Bootstrap Glyphicon to Input Box1
 
< 0.1%
How to remove focus border (outline) around text/input boxes? (Chrome)1
 
< 0.1%
Other values (11925)11925
99.9%
2021-05-21T12:07:24.530364image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to4252
 
4.2%
how3728
 
3.7%
in3637
 
3.6%
a3626
 
3.6%
the2507
 
2.5%
and1798
 
1.8%
is1568
 
1.5%
i1451
 
1.4%
of1366
 
1.3%
what1335
 
1.3%
Other values (8901)76489
75.2%

Most occurring characters

ValueCountFrequency (%)
89829
14.9%
e52406
 
8.7%
t41400
 
6.9%
o38077
 
6.3%
a36970
 
6.1%
n35151
 
5.8%
i34919
 
5.8%
r29138
 
4.8%
s26213
 
4.4%
l17461
 
2.9%
Other values (106)199825
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter448000
74.5%
Space Separator89829
 
14.9%
Uppercase Letter41238
 
6.9%
Other Punctuation14451
 
2.4%
Decimal Number2072
 
0.3%
Dash Punctuation1532
 
0.3%
Open Punctuation1197
 
0.2%
Close Punctuation1192
 
0.2%
Math Symbol1154
 
0.2%
Connector Punctuation435
 
0.1%
Other values (7)289
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
H4495
 
10.9%
S4314
 
10.5%
I3177
 
7.7%
C2706
 
6.6%
W2549
 
6.2%
P2541
 
6.2%
A2171
 
5.3%
T1842
 
4.5%
L1657
 
4.0%
D1577
 
3.8%
Other values (18)14209
34.5%
ValueCountFrequency (%)
e52406
11.7%
t41400
 
9.2%
o38077
 
8.5%
a36970
 
8.3%
n35151
 
7.8%
i34919
 
7.8%
r29138
 
6.5%
s26213
 
5.9%
l17461
 
3.9%
c17241
 
3.8%
Other values (17)119024
26.6%
ValueCountFrequency (%)
?6417
44.4%
.1784
 
12.3%
"1369
 
9.5%
'1303
 
9.0%
,1140
 
7.9%
:943
 
6.5%
/869
 
6.0%
#201
 
1.4%
*106
 
0.7%
@93
 
0.6%
Other values (7)226
 
1.6%
ValueCountFrequency (%)
0409
19.7%
1355
17.1%
2299
14.4%
3208
10.0%
4196
9.5%
8157
 
7.6%
6154
 
7.4%
5142
 
6.9%
798
 
4.7%
954
 
2.6%
ValueCountFrequency (%)
+601
52.1%
>186
 
16.1%
<173
 
15.0%
=170
 
14.7%
~12
 
1.0%
|11
 
1.0%
1
 
0.1%
ValueCountFrequency (%)
2
25.0%
👩2
25.0%
1
12.5%
1
12.5%
👧1
12.5%
👦1
12.5%
ValueCountFrequency (%)
-1511
98.6%
11
 
0.7%
10
 
0.7%
ValueCountFrequency (%)
(1098
91.7%
[78
 
6.5%
{21
 
1.8%
ValueCountFrequency (%)
)1097
92.0%
]74
 
6.2%
}21
 
1.8%
ValueCountFrequency (%)
`133
92.4%
^11
 
7.6%
ValueCountFrequency (%)
15
75.0%
5
 
25.0%
ValueCountFrequency (%)
17
51.5%
16
48.5%
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
ValueCountFrequency (%)
89829
100.0%
ValueCountFrequency (%)
$79
100.0%
ValueCountFrequency (%)
_435
100.0%
ValueCountFrequency (%)
ܫ1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin489234
81.4%
Common112147
 
18.6%
Greek3
 
< 0.1%
Inherited3
 
< 0.1%
Syriac1
 
< 0.1%
Cyrillic1
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
89829
80.1%
?6417
 
5.7%
.1784
 
1.6%
-1511
 
1.3%
"1369
 
1.2%
'1303
 
1.2%
,1140
 
1.0%
(1098
 
1.0%
)1097
 
1.0%
:943
 
0.8%
Other values (49)5656
 
5.0%
ValueCountFrequency (%)
e52406
 
10.7%
t41400
 
8.5%
o38077
 
7.8%
a36970
 
7.6%
n35151
 
7.2%
i34919
 
7.1%
r29138
 
6.0%
s26213
 
5.4%
l17461
 
3.6%
c17241
 
3.5%
Other values (42)160258
32.8%
ValueCountFrequency (%)
Θ2
66.7%
π1
33.3%
ValueCountFrequency (%)
ܫ1
100.0%
ValueCountFrequency (%)
3
100.0%
ValueCountFrequency (%)
Ө1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII601294
> 99.9%
Punctuation80
 
< 0.1%
None8
 
< 0.1%
Geometric Shapes3
 
< 0.1%
Syriac1
 
< 0.1%
Misc Symbols1
 
< 0.1%
Cyrillic1
 
< 0.1%
Arrows1
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
89829
14.9%
e52406
 
8.7%
t41400
 
6.9%
o38077
 
6.3%
a36970
 
6.1%
n35151
 
5.8%
i34919
 
5.8%
r29138
 
4.8%
s26213
 
4.4%
l17461
 
2.9%
Other values (85)199730
33.2%
ValueCountFrequency (%)
17
21.2%
16
20.0%
15
18.8%
11
13.8%
10
12.5%
5
 
6.2%
3
 
3.8%
2
 
2.5%
1
 
1.2%
ValueCountFrequency (%)
Θ2
25.0%
👩2
25.0%
π1
12.5%
👧1
12.5%
👦1
12.5%
·1
12.5%
ValueCountFrequency (%)
2
66.7%
1
33.3%
ValueCountFrequency (%)
ܫ1
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
Ө1
100.0%
ValueCountFrequency (%)
1
100.0%

Tags
Categorical

HIGH CARDINALITY

Distinct10083
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Memory size93.4 KiB
<git>
 
148
<javascript>
 
57
<python>
 
41
<html><css>
 
32
<git><github>
 
30
Other values (10078)
11627 

Length

Max length102
Median length31
Mean length32.78583997
Min length3

Characters and Unicode

Total characters391299
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9439 ?
Unique (%)79.1%

Sample

1st row<c#><linq><linq-to-objects>
2nd row<javascript><jquery><requirejs>
3rd row<algorithm><matrix><multidimensional-array>
4th row<sql><sql-server><tsql>
5th row<android><github><intellij-idea><android-studio>
ValueCountFrequency (%)
<git>148
 
1.2%
<javascript>57
 
0.5%
<python>41
 
0.3%
<html><css>32
 
0.3%
<git><github>30
 
0.3%
<javascript><jquery>28
 
0.2%
<docker>27
 
0.2%
<python><pandas><dataframe>23
 
0.2%
<javascript><arrays>21
 
0.2%
<node.js>20
 
0.2%
Other values (10073)11508
96.4%
2021-05-21T12:07:25.045301image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
git148
 
1.2%
javascript57
 
0.5%
python41
 
0.3%
html><css32
 
0.3%
git><github30
 
0.3%
javascript><jquery28
 
0.2%
docker27
 
0.2%
python><pandas><dataframe23
 
0.2%
javascript><arrays21
 
0.2%
regex20
 
0.2%
Other values (10071)11508
96.4%

Most occurring characters

ValueCountFrequency (%)
<38581
 
9.9%
>38581
 
9.9%
a25849
 
6.6%
e25742
 
6.6%
t24415
 
6.2%
i23650
 
6.0%
o21129
 
5.4%
r20965
 
5.4%
n20599
 
5.3%
s20499
 
5.2%
Other values (32)131289
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter297624
76.1%
Math Symbol78596
 
20.1%
Dash Punctuation10927
 
2.8%
Decimal Number2174
 
0.6%
Other Punctuation1978
 
0.5%

Most frequent character per category

ValueCountFrequency (%)
a25849
 
8.7%
e25742
 
8.6%
t24415
 
8.2%
i23650
 
7.9%
o21129
 
7.1%
r20965
 
7.0%
n20599
 
6.9%
s20499
 
6.9%
c15187
 
5.1%
l12312
 
4.1%
Other values (16)87277
29.3%
ValueCountFrequency (%)
2400
18.4%
0337
15.5%
1302
13.9%
3259
11.9%
4216
9.9%
6190
8.7%
8185
8.5%
5149
 
6.9%
797
 
4.5%
939
 
1.8%
ValueCountFrequency (%)
<38581
49.1%
>38581
49.1%
+1434
 
1.8%
ValueCountFrequency (%)
.1356
68.6%
#622
31.4%
ValueCountFrequency (%)
-10927
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin297624
76.1%
Common93675
 
23.9%

Most frequent character per script

ValueCountFrequency (%)
a25849
 
8.7%
e25742
 
8.6%
t24415
 
8.2%
i23650
 
7.9%
o21129
 
7.1%
r20965
 
7.0%
n20599
 
6.9%
s20499
 
6.9%
c15187
 
5.1%
l12312
 
4.1%
Other values (16)87277
29.3%
ValueCountFrequency (%)
<38581
41.2%
>38581
41.2%
-10927
 
11.7%
+1434
 
1.5%
.1356
 
1.4%
#622
 
0.7%
2400
 
0.4%
0337
 
0.4%
1302
 
0.3%
3259
 
0.3%
Other values (6)876
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII391299
100.0%

Most frequent character per block

ValueCountFrequency (%)
<38581
 
9.9%
>38581
 
9.9%
a25849
 
6.6%
e25742
 
6.6%
t24415
 
6.2%
i23650
 
6.0%
o21129
 
5.4%
r20965
 
5.4%
n20599
 
5.3%
s20499
 
5.2%
Other values (32)131289
33.6%

AnswerCount
Real number (ℝ≥0)

Distinct118
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.9777964
Minimum1
Maximum518
Zeros0
Zeros (%)0.0%
Memory size93.4 KiB
2021-05-21T12:07:25.263135image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median13
Q320
95-th percentile37
Maximum518
Range517
Interquartile range (IQR)12

Descriptive statistics

Standard deviation14.4488636
Coefficient of variation (CV)0.9043089075
Kurtosis209.2596501
Mean15.9777964
Median Absolute Deviation (MAD)6
Skewness8.807920863
Sum190695
Variance208.7696595
MonotocityNot monotonic
2021-05-21T12:07:25.450203image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10637
 
5.3%
8636
 
5.3%
12597
 
5.0%
7596
 
5.0%
9593
 
5.0%
11579
 
4.9%
6543
 
4.5%
5541
 
4.5%
14500
 
4.2%
13481
 
4.0%
Other values (108)6232
52.2%
ValueCountFrequency (%)
1102
 
0.9%
2224
1.9%
3349
2.9%
4452
3.8%
5541
4.5%
ValueCountFrequency (%)
5181
< 0.1%
4071
< 0.1%
3201
< 0.1%
2961
< 0.1%
2141
< 0.1%

CommentCount
Real number (ℝ≥0)

ZEROS

Distinct46
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.537494763
Minimum0
Maximum61
Zeros2501
Zeros (%)21.0%
Memory size93.4 KiB
2021-05-21T12:07:25.628989image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile11
Maximum61
Range61
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.167143976
Coefficient of variation (CV)1.177992974
Kurtosis19.19526346
Mean3.537494763
Median Absolute Deviation (MAD)2
Skewness3.121828554
Sum42220
Variance17.36508891
MonotocityNot monotonic
2021-05-21T12:07:25.800382image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
02501
21.0%
11941
16.3%
21634
13.7%
31402
11.7%
41080
9.0%
5826
 
6.9%
6636
 
5.3%
7482
 
4.0%
8339
 
2.8%
9286
 
2.4%
Other values (36)808
 
6.8%
ValueCountFrequency (%)
02501
21.0%
11941
16.3%
21634
13.7%
31402
11.7%
41080
9.0%
ValueCountFrequency (%)
611
< 0.1%
571
< 0.1%
551
< 0.1%
501
< 0.1%
471
< 0.1%

FavoriteCount
Real number (ℝ≥0)

Distinct886
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210.6356933
Minimum81
Maximum11317
Zeros0
Zeros (%)0.0%
Memory size93.4 KiB
2021-05-21T12:07:26.249671image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum81
5-th percentile84
Q199
median130
Q3207
95-th percentile541
Maximum11317
Range11236
Interquartile range (IQR)108

Descriptive statistics

Standard deviation339.6237681
Coefficient of variation (CV)1.612375199
Kurtosis303.2504586
Mean210.6356933
Median Absolute Deviation (MAD)39
Skewness13.81748629
Sum2513937
Variance115344.3038
MonotocityNot monotonic
2021-05-21T12:07:26.429367image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82203
 
1.7%
85195
 
1.6%
81194
 
1.6%
86184
 
1.5%
84179
 
1.5%
87178
 
1.5%
88176
 
1.5%
92174
 
1.5%
90174
 
1.5%
83172
 
1.4%
Other values (876)10106
84.7%
ValueCountFrequency (%)
81194
1.6%
82203
1.7%
83172
1.4%
84179
1.5%
85195
1.6%
ValueCountFrequency (%)
113171
< 0.1%
110101
< 0.1%
74041
< 0.1%
71241
< 0.1%
66941
< 0.1%

ClosedDate
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct1369
Distinct (%)99.6%
Missing10560
Missing (%)88.5%
Memory size93.4 KiB
2020-02-02 13:32:29
 
3
2020-01-31 06:45:01
 
2
2020-01-31 06:45:02
 
2
2020-01-31 06:44:36
 
2
2020-02-02 13:30:21
 
2
Other values (1364)
1364 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters26125
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1364 ?
Unique (%)99.2%

Sample

1st row2011-10-07 13:02:51
2nd row2012-02-29 16:53:12
3rd row2018-03-28 08:29:45
4th row2011-11-28 18:39:35
5th row2011-10-02 14:01:25
ValueCountFrequency (%)
2020-02-02 13:32:293
 
< 0.1%
2020-01-31 06:45:012
 
< 0.1%
2020-01-31 06:45:022
 
< 0.1%
2020-01-31 06:44:362
 
< 0.1%
2020-02-02 13:30:212
 
< 0.1%
2019-12-02 16:15:011
 
< 0.1%
2013-11-26 00:13:351
 
< 0.1%
2016-12-23 03:04:451
 
< 0.1%
2015-03-20 16:09:271
 
< 0.1%
2009-07-01 20:54:211
 
< 0.1%
Other values (1359)1359
 
11.4%
(Missing)10560
88.5%
2021-05-21T12:07:26.844750image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-01-3121
 
0.8%
2021-02-1010
 
0.4%
2020-02-026
 
0.2%
2013-09-064
 
0.1%
2019-12-184
 
0.1%
2012-02-164
 
0.1%
2017-04-074
 
0.1%
2011-10-054
 
0.1%
2013-01-113
 
0.1%
2014-12-233
 
0.1%
Other values (2430)2687
97.7%

Most occurring characters

ValueCountFrequency (%)
04554
17.4%
14136
15.8%
23629
13.9%
-2750
10.5%
:2750
10.5%
31474
 
5.6%
1375
 
5.3%
41260
 
4.8%
51200
 
4.6%
6783
 
3.0%
Other values (3)2214
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number19250
73.7%
Dash Punctuation2750
 
10.5%
Other Punctuation2750
 
10.5%
Space Separator1375
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
04554
23.7%
14136
21.5%
23629
18.9%
31474
 
7.7%
41260
 
6.5%
51200
 
6.2%
6783
 
4.1%
8749
 
3.9%
9745
 
3.9%
7720
 
3.7%
ValueCountFrequency (%)
-2750
100.0%
ValueCountFrequency (%)
1375
100.0%
ValueCountFrequency (%)
:2750
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common26125
100.0%

Most frequent character per script

ValueCountFrequency (%)
04554
17.4%
14136
15.8%
23629
13.9%
-2750
10.5%
:2750
10.5%
31474
 
5.6%
1375
 
5.3%
41260
 
4.8%
51200
 
4.6%
6783
 
3.0%
Other values (3)2214
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII26125
100.0%

Most frequent character per block

ValueCountFrequency (%)
04554
17.4%
14136
15.8%
23629
13.9%
-2750
10.5%
:2750
10.5%
31474
 
5.6%
1375
 
5.3%
41260
 
4.8%
51200
 
4.6%
6783
 
3.0%
Other values (3)2214
8.5%

CommunityOwnedDate
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct556
Distinct (%)100.0%
Missing11379
Missing (%)95.3%
Memory size93.4 KiB
2009-02-18 18:32:02
 
1
2013-01-22 14:48:20
 
1
2008-10-21 21:02:34
 
1
2013-09-28 05:29:12
 
1
2008-10-29 17:09:40
 
1
Other values (551)
551 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters10564
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique556 ?
Unique (%)100.0%

Sample

1st row2013-11-25 20:37:32
2nd row2009-01-07 20:14:42
3rd row2008-10-03 13:35:43
4th row2009-09-14 21:00:18
5th row2008-10-23 22:39:45
ValueCountFrequency (%)
2009-02-18 18:32:021
 
< 0.1%
2013-01-22 14:48:201
 
< 0.1%
2008-10-21 21:02:341
 
< 0.1%
2013-09-28 05:29:121
 
< 0.1%
2008-10-29 17:09:401
 
< 0.1%
2010-02-16 18:51:141
 
< 0.1%
2008-09-30 06:28:531
 
< 0.1%
2012-11-12 21:30:591
 
< 0.1%
2009-01-11 20:02:421
 
< 0.1%
2012-08-01 15:16:151
 
< 0.1%
Other values (546)546
 
4.6%
(Missing)11379
95.3%
2021-05-21T12:07:27.238822image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2008-09-1916
 
1.4%
2010-01-227
 
0.6%
2008-10-015
 
0.4%
2008-09-244
 
0.4%
2009-01-074
 
0.4%
2008-09-174
 
0.4%
2008-09-164
 
0.4%
2008-10-174
 
0.4%
2009-01-023
 
0.3%
2008-09-153
 
0.3%
Other values (990)1058
95.1%

Most occurring characters

ValueCountFrequency (%)
02101
19.9%
11542
14.6%
21419
13.4%
-1112
10.5%
:1112
10.5%
556
 
5.3%
3522
 
4.9%
9462
 
4.4%
5424
 
4.0%
4418
 
4.0%
Other values (3)896
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7784
73.7%
Dash Punctuation1112
 
10.5%
Other Punctuation1112
 
10.5%
Space Separator556
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
02101
27.0%
11542
19.8%
21419
18.2%
3522
 
6.7%
9462
 
5.9%
5424
 
5.4%
4418
 
5.4%
8370
 
4.8%
7263
 
3.4%
6263
 
3.4%
ValueCountFrequency (%)
-1112
100.0%
ValueCountFrequency (%)
556
100.0%
ValueCountFrequency (%)
:1112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10564
100.0%

Most frequent character per script

ValueCountFrequency (%)
02101
19.9%
11542
14.6%
21419
13.4%
-1112
10.5%
:1112
10.5%
556
 
5.3%
3522
 
4.9%
9462
 
4.4%
5424
 
4.0%
4418
 
4.0%
Other values (3)896
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10564
100.0%

Most frequent character per block

ValueCountFrequency (%)
02101
19.9%
11542
14.6%
21419
13.4%
-1112
10.5%
:1112
10.5%
556
 
5.3%
3522
 
4.9%
9462
 
4.4%
5424
 
4.0%
4418
 
4.0%
Other values (3)896
8.5%

ContentLicense
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size93.4 KiB
CC BY-SA 3.0
6851 
CC BY-SA 4.0
3677 
CC BY-SA 2.5
1407 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters143220
Distinct characters13
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCC BY-SA 4.0
2nd rowCC BY-SA 3.0
3rd rowCC BY-SA 4.0
4th rowCC BY-SA 3.0
5th rowCC BY-SA 3.0
ValueCountFrequency (%)
CC BY-SA 3.06851
57.4%
CC BY-SA 4.03677
30.8%
CC BY-SA 2.51407
 
11.8%
2021-05-21T12:07:27.538238image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-21T12:07:27.645494image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
by-sa11935
33.3%
cc11935
33.3%
3.06851
19.1%
4.03677
 
10.3%
2.51407
 
3.9%

Most occurring characters

ValueCountFrequency (%)
C23870
16.7%
23870
16.7%
B11935
8.3%
Y11935
8.3%
-11935
8.3%
S11935
8.3%
A11935
8.3%
.11935
8.3%
010528
7.4%
36851
 
4.8%
Other values (3)6491
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter71610
50.0%
Space Separator23870
 
16.7%
Decimal Number23870
 
16.7%
Dash Punctuation11935
 
8.3%
Other Punctuation11935
 
8.3%

Most frequent character per category

ValueCountFrequency (%)
C23870
33.3%
B11935
16.7%
Y11935
16.7%
S11935
16.7%
A11935
16.7%
ValueCountFrequency (%)
010528
44.1%
36851
28.7%
43677
 
15.4%
21407
 
5.9%
51407
 
5.9%
ValueCountFrequency (%)
23870
100.0%
ValueCountFrequency (%)
-11935
100.0%
ValueCountFrequency (%)
.11935
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin71610
50.0%
Common71610
50.0%

Most frequent character per script

ValueCountFrequency (%)
23870
33.3%
-11935
16.7%
.11935
16.7%
010528
14.7%
36851
 
9.6%
43677
 
5.1%
21407
 
2.0%
51407
 
2.0%
ValueCountFrequency (%)
C23870
33.3%
B11935
16.7%
Y11935
16.7%
S11935
16.7%
A11935
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII143220
100.0%

Most frequent character per block

ValueCountFrequency (%)
C23870
16.7%
23870
16.7%
B11935
8.3%
Y11935
8.3%
-11935
8.3%
S11935
8.3%
A11935
8.3%
.11935
8.3%
010528
7.4%
36851
 
4.8%
Other values (3)6491
 
4.5%

Interactions

2021-05-21T12:07:01.609503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:01.804981image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:02.100192image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:02.364485image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:02.563558image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:02.783969image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:02.982938image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:03.173872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:03.371612image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:03.573272image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:03.754089image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:03.938885image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:04.137722image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:04.328836image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:04.516164image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:04.697074image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:04.869114image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:05.035039image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:05.201180image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:05.367171image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:05.565991image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:05.765114image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:05.936334image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:06.108434image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:06.278241image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:06.432293image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:06.590463image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:06.750524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:06.917621image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:07.092211image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:07.252810image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:07.421688image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:07.583734image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:07.751807image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:08.480193image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:08.653310image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:08.815442image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:08.991144image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:09.156700image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:09.327730image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:09.504713image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:09.704409image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:09.921861image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:10.149252image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:10.339745image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:10.556132image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:10.760623image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:10.953071image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:11.142601image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:11.394892image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:11.603333image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:11.770885image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:11.935480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:12.136907image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:12.316426image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:12.476998image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:12.682448image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:12.883912image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:13.062471image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:13.219013image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:13.378587image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:13.602987image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:13.831378image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:14.090687image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:14.333037image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:14.572396image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:14.813750image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:15.059096image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:15.266542image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:15.493933image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:15.737284image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-21T12:07:15.964675image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-05-21T12:07:27.774536image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-21T12:07:28.065603image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-21T12:07:28.363806image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-21T12:07:28.662591image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-21T12:07:28.908932image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-21T12:07:16.487138image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-21T12:07:17.253239image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-21T12:07:17.744627image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-21T12:07:18.070451image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IdPostTypeIdAcceptedAnswerIdParentIdCreationDateDeletionDateScoreViewCountBodyOwnerUserIdOwnerDisplayNameLastEditorUserIdLastEditorDisplayNameLastEditDateLastActivityDateTitleTagsAnswerCountCommentCountFavoriteCountClosedDateCommunityOwnedDateContentLicense
0412441233505.0NaN2008-09-03 06:30:31NaN690283067<p>I found an example in the <a href="http://msdn2.microsoft.com/en-us/bb330936.aspx" rel="noreferrer">VS2008 Examples</a> for Dynamic LINQ that allows you to use a sql-like string (e.g. <code>OrderBy("Name, Age DESC"))</code> for ordering. Unfortunately, the method included only works on <code>IQueryable&lt;T&gt;</code>. Is there any way to get this functionality on <code>IEnumerable&lt;T&gt;</code>?</p>\n1786.0John Sheehan5519709.0John Sheehan2020-05-06 08:43:382020-09-27 11:39:24Dynamic LINQ OrderBy on IEnumerable<T> / IQueryable<T><c#><linq><linq-to-objects>201440NaNNaNCC BY-SA 4.0
1831508818479953.0NaN2011-11-29 17:30:29NaN302108680<p>RequireJS seems to do something internally that caches required javascript files. If I make a change to one of the required files, I have to rename the file in order for the changes to be applied. </p>\n\n<p>The common trick of appending a version number as a querystring param to the end of the filename does not work with requirejs <code>&lt;script src="jsfile.js?v2"&gt;&lt;/script&gt;</code> </p>\n\n<p>What I am looking for is a way to prevent this internal cacheing of RequireJS required scripts without having to rename my script files every time they are updated.</p>\n\n<p><strong>Cross-Platform Solution:</strong></p>\n\n<p>I am now using <code>urlArgs: "bust=" + (new Date()).getTime()</code> for automatic cache-busting during development and <code>urlArgs: "bust=v2"</code> for production where I increment the hard-coded version num after rolling out an updated required script.</p>\n\n<p><strong>Note:</strong></p>\n\n<p>@Dustin Getz mentioned in a recent answer that Chrome Developer Tools will drop breakpoints during debugging when Javascript files are continuously refreshed like this. One workaround is to write <code>debugger;</code> in code to trigger a breakpoint in most Javascript debuggers.</p>\n\n<p><strong>Server-Specific Solutions:</strong></p>\n\n<p>For specific solutions that may work better for your server environment such as Node or Apache, see some of the answers below.</p>\n285714.0NaN285714.0NaN2013-12-11 17:09:202018-09-12 07:43:01Prevent RequireJS from Caching Required Scripts<javascript><jquery><requirejs>126116NaNNaNCC BY-SA 3.0
242519142535.0NaN2008-09-03 20:34:33NaN323333094<p>Inspired by <a href="https://devblogs.microsoft.com/oldnewthing/20080902-00/?p=21003" rel="noreferrer">Raymond Chen's post</a>, say you have a 4x4 two dimensional array, write a function that rotates it 90 degrees. Raymond links to a solution in pseudo code, but I'd like to see some real world stuff.</p>\n\n<pre><code>[1][2][3][4]\n[5][6][7][8]\n[9][0][1][2]\n[3][4][5][6]\n</code></pre>\n\n<p>Becomes:</p>\n\n<pre><code>[3][9][5][1]\n[4][0][6][2]\n[5][1][7][3]\n[6][2][8][4]\n</code></pre>\n\n<p><strong>Update</strong>: Nick's answer is the most straightforward, but is there a way to do it better than n^2? What if the matrix was 10000x10000?</p>\n736.0swilliams4415734.0Jakub Šturc2020-02-22 17:25:332020-12-16 18:32:49How do you rotate a two dimensional array?<algorithm><matrix><multidimensional-array>6313221NaN2013-11-25 20:37:32CC BY-SA 4.0
342648142655.0NaN2008-09-03 21:32:02NaN1202953268<p>What is the best way to get <code>IDENTITY</code> of inserted row?</p>\n\n<p>I know about <code>@@IDENTITY</code> and <code>IDENT_CURRENT</code> and <code>SCOPE_IDENTITY</code> but don't understand the pros and cons attached to each.</p>\n\n<p>Can someone please explain the differences and when I should be using each?</p>\n1583.0Oded3876565.0NaN2018-03-26 08:14:432021-01-14 02:35:41Best way to get identity of inserted row?<sql><sql-server><tsql>144348NaNNaNCC BY-SA 3.0
416644946116682570.0NaN2013-05-20 07:53:02NaN210321230<p>I am trying to synchronise a project that I have on in my Android Studio folder to GitHub, but I am not fully sure what to do other than adding my credentials in the options menu. Could someone give me a quick guide, please?</p>\n1342580.0NaN63550.0NaN2016-09-16 04:18:582021-02-28 21:59:41How do you synchronise projects to GitHub with Android Studio?<android><github><intellij-idea><android-studio>13095NaNNaNCC BY-SA 3.0
54501515686237.0NaN2008-09-05 00:12:01NaN13931312306<p>Given a string of JSON data, how can I safely turn that string into a JavaScript object?</p>\n\n<p>Obviously I can do this unsafely with something like:</p>\n\n<pre><code>var obj = eval("(" + json + ')');\n</code></pre>\n\n<p>but that leaves me vulnerable to the JSON string containing other code, which it seems very dangerous to simply eval.</p>\n797.0Matt Sheppard860099.0NaN2020-09-21 12:15:562020-09-21 12:15:56Safely turning a JSON string into an object<javascript><json><parsing>285224NaNNaNCC BY-SA 4.0
6105641773973.0NaN2008-08-14 01:43:04NaN585341505<p>I'm trying out <strong>Git on Windows</strong>. I got to the point of trying "git commit" and I got this error:</p>\n\n<blockquote>\n <p>Terminal is dumb but no VISUAL nor\n EDITOR defined. Please supply the\n message using either -m or -F option.</p>\n</blockquote>\n\n<p>So I figured out I need to have an environment variable called EDITOR. No problem. I set it to point to Notepad. That worked, almost. The default commit message opens in Notepad. But Notepad doesn't support bare line feeds. I went out and got <a href="http://notepad-plus.sourceforge.net/uk/site.htm" rel="noreferrer">Notepad++</a>, but I can't figure out how to get Notepad++ set up as the <code>%EDITOR%</code> in such a way that it works with Git as expected.</p>\n\n<p>I'm not married to Notepad++. At this point I don't mind what editor I use. I just want to be able to <strong>type commit messages in an editor</strong> rather than the command line (with <code>-m</code>).</p>\n\n<p>Those of you using Git on Windows: What tool do you use to edit your commit messages, and what did you have to do to make it work?</p>\n437.0Patrick McElhaney1002260.0ΤΖΩΤΖΙΟΥ2016-12-24 17:26:042020-12-18 10:49:47How can I set up an editor to work with Git on Windows?<windows><git><cygwin><editor>346289NaNNaNCC BY-SA 2.5
724965125203.0NaN2008-08-24 10:43:17NaN4320579<p>OK, I know there have already been questions about <a href="https://stackoverflow.com/questions/4303/why-should-i-practice-test-driven-development-and-how-should-i-start">getting started with TDD</a>.. However, I guess I kind of know the general concensus is to <em>just do it</em> , However, I seem to have the following problems getting my head into the game:</p>\n\n<ul>\n<li>When working with collections, do will still test for obvious add/remove/inserts successful, even when based on Generics etc where we kind of "know" its going to work?</li>\n<li>Some tests seem to take forever to implement.. Such as when working with string output, is there a "better" way to go about this sort of thing? (e.g. test the object model before parsing, break parsing down into small ops and test there) In my mind you should always test the "end result" but that can vary wildly and be tedious to set up.</li>\n<li>I don't have a testing framework to use (work wont pay for one) so I can "practice" more. Are there any good ones that are free for commercial use? (at the moment I am using good 'ol <em>Debug.Assert</em> :)</li>\n<li>Probably the biggest.. Sometimes I don't know what to expect <em>NOT</em> to happen.. I mean, you get your green light but I am always concerned that I may be missing a test.. Do you dig deeper to try and break the code, or leave it be and wait for it all fall over later (which will cost more)..</li>\n</ul>\n\n<p>So basically what I am looking for here is not a " <em>just do it</em> " but more " <em>I did this, had problems with this, solved them by this</em> ".. The <strong>personal</strong> experience :)</p>\n832.0Rob Cooper-1.0Chris Fournier2017-05-23 12:34:452011-08-27 20:33:46Beginning TDD - Challenges? Solutions? Recommendations?<unit-testing><language-agnostic><tdd>1101172011-10-07 13:02:51NaNCC BY-SA 2.5
826255160396.0NaN2008-08-25 15:29:39NaN16368763<p>What JavaScript keywords (function names, variables, etc) are reserved?</p>\n399.0Titanous122643.0NaN2018-01-15 00:16:532021-01-28 11:57:03Reserved keywords in JavaScript<javascript><reserved-words>8194NaNNaNCC BY-SA 3.0
958306158446.0NaN2008-09-12 04:36:51NaN11798489<p>I am trying to determine the best time efficient algorithm to accomplish the task described below.</p>\n\n<p>I have a set of records. For this set of records I have connection data which indicates how pairs of records from this set connect to one another. This basically represents an undirected graph, with the records being the vertices and the connection data the edges.</p>\n\n<p>All of the records in the set have connection information (i.e. no orphan records are present; each record in the set connects to one or more other records in the set).</p>\n\n<p>I want to choose any two records from the set and be able to show all simple paths between the chosen records. By "simple paths" I mean the paths which do not have repeated records in the path (i.e. finite paths only).</p>\n\n<p>Note: The two chosen records will always be different (i.e. start and end vertex will never be the same; no cycles).</p>\n\n<p>For example:</p>\n\n<pre>\n If I have the following records:\n A, B, C, D, E\n\n and the following represents the connections: \n (A,B),(A,C),(B,A),(B,D),(B,E),(B,F),(C,A),(C,E),\n (C,F),(D,B),(E,C),(E,F),(F,B),(F,C),(F,E)\n\n [where (A,B) means record A connects to record B]\n</pre>\n\n<p>If I chose B as my starting record and E as my ending record, I would want to find all simple paths through the record connections that would connect record B to record E.</p>\n\n<pre>\n All paths connecting B to E:\n B->E\n B->F->E\n B->F->C->E\n B->A->C->E\n B->A->C->F->E\n</pre>\n\n<p>This is an example, in practice I may have sets containing hundreds of thousands of records.</p>\n3534.0Robert1571709.0cynicalman2017-03-23 00:30:312017-03-23 00:30:31Graph Algorithm To Find All Connections Between Two Arbitrary Vertices<algorithm><language-agnostic><graph-theory>16394NaNNaNCC BY-SA 3.0

Last rows

IdPostTypeIdAcceptedAnswerIdParentIdCreationDateDeletionDateScoreViewCountBodyOwnerUserIdOwnerDisplayNameLastEditorUserIdLastEditorDisplayNameLastEditDateLastActivityDateTitleTagsAnswerCountCommentCountFavoriteCountClosedDateCommunityOwnedDateContentLicense
1192541060382143729857.0NaN2016-12-09 12:20:55NaN245414074<p>conda 4.2.13\nMacOSX 10.12.1</p>\n\n<p>I am trying to install packages from <code>pip</code> to a fresh environment (virtual) created using anaconda. <a href="http://conda.pydata.org/docs/using/pkgs.html#install-non-conda-packages" rel="noreferrer">In the Anaconda docs</a> it says this is perfectly fine. It is done the same way as for virtualenv. </p>\n\n<blockquote>\n <p>Activate the environment where you want to put the program, then pip install a program...</p>\n</blockquote>\n\n<p>I created an empty environment in Ananconda like this:</p>\n\n<pre><code>conda create -n shrink_venv\n</code></pre>\n\n<p>Activate it:</p>\n\n<pre><code>source activate shrink_venv\n</code></pre>\n\n<p>I then can see in the terminal that I am working in my env <code>(shrink_venv)</code>. Problem is coming up, when I try to install a package using <code>pip</code>:</p>\n\n<pre><code>(shrink_venv): pip install Pillow\n\nRequirement already satisfied (use --upgrade to upgrade): Pillow in /Library/Python/2.7/site-packages\n</code></pre>\n\n<p>So I can see it thinks the requirement is satisfied from the system-wide package. So it seems the environment is not working correctly, definitely not like it said in the docs. Am I doing something wrong here?</p>\n\n<p>Just a note, I know you can use <code>conda install</code> for the packages, but I have had an issue with Pillow from anaconda, so I wanted to get it from <code>pip</code>, and since the docs say that is fine.</p>\n\n<p>Output of <code>which -a pip</code>:</p>\n\n<pre><code>/usr/local/bin/pip\n/Users/my_user/anaconda/bin/pip\n</code></pre>\n\n<p>** UPDATE **\nI see this is pretty common issue. What I have found is that the conda env doesn't play well with the PYTHONPATH. The system seems to always look in the PYTHONPATH locations even when you're using a conda environment. Now, I always run <code>unset PYTHONPATH</code> when using a conda environment, and it works much better. I'm on a mac.</p>\n959306.0NaN959306.0NaN2018-10-24 14:02:242021-02-10 19:20:52Using Pip to install packages to Anaconda Environment<python><pip><anaconda><environment>1510131NaNNaNCC BY-SA 4.0
11926408017721NaNNaN2016-11-25 09:39:10NaN609307159<p>What is the difference between <code>ports</code> and <code>expose</code> options in <code>docker-compose.yml</code></p>\n84143.0NaN4516110.0NaN2019-02-26 09:47:222021-04-10 03:07:19What is the difference between docker-compose ports vs expose<docker><docker-compose>50133NaNNaNCC BY-SA 4.0
1192740987309140988672.0NaN2016-12-06 03:23:50NaN184323838<p>This is an example from Google Adsense application page. The loading screen displayed before the main page showed after.</p>\n\n<p><a href="https://i.stack.imgur.com/ngk1r.gif" rel="noreferrer"><img src="https://i.stack.imgur.com/ngk1r.gif" alt="enter image description here"></a></p>\n\n<p>I don't know how to do the same thing with React because if I make loading screen rendered by React component, it doesn't display while page is loading because it has to wait for DOM rendered before.</p>\n\n<p><strong>Updated</strong>:</p>\n\n<p>I made an example of my approach by putting screen loader in <code>index.html</code> and remove it in React <code>componentDidMount()</code> lifecycle method.</p>\n\n<p><a href="https://nguyenbathanh.github.io" rel="noreferrer">Example</a> and <a href="https://github.com/nguyenbathanh/react-loading-screen" rel="noreferrer">react-loading-screen</a>.</p>\n1550476.0NaN11673149.0NaN2019-08-17 08:10:242020-11-13 05:16:38React - Display loading screen while DOM is rendering?<reactjs><asynchronous><redux>19399NaNNaNCC BY-SA 4.0
1192840703675140704083.0NaN2016-11-20 11:51:03NaN19341750<p>Came to know that from <strong>React v15.3.0</strong>, we have a new base class called <strong>PureComponent</strong> to extend with <strong>PureRenderMixin</strong> built-in. What I understand is that, under the hood this employs a shallow comparison of props inside <code>shouldComponentUpdate</code>.</p>\n<p>Now we have 3 ways to define a React component:</p>\n<ol>\n<li>Functional stateless component which doesn't extend any class</li>\n<li>A component that extends <code>PureComponent</code> class</li>\n<li>A normal component that extends <code>Component</code> class</li>\n</ol>\n<p>Some time back we used to call stateless components as Pure Components, or even Dumb Components. Seems like the whole definition of the word &quot;pure&quot; has now changed in React.</p>\n<p>Although I understand basic differences between these three, I am still not sure <strong>when to choose what</strong>. Also what are the performance impacts and trade-offs of each?</p>\n<hr />\n<h3><strong>Update</strong>:</h3>\n<p>These are the question I expect to get clarified:</p>\n<ul>\n<li>Should I choose to define my simple components as functional (for the sake of simplicity) or extend <code>PureComponent</code> class (for performance sake)?</li>\n<li>Is the performance boost that I get a real trade-off for the\nsimplicity I lost?</li>\n<li>Would I ever need to extend the normal <code>Component</code> class when I can always use <code>PureComponent</code> for better performance?</li>\n</ul>\n5069226.0NaN-1.0NaN2020-06-20 09:12:552019-02-15 18:01:24React functional stateless component, PureComponent, Component; what are the differences and when should we use what?<javascript><reactjs><ecmascript-6>30120NaNNaNCC BY-SA 3.0
1192940729535150255986.0NaN2016-11-21 21:15:29NaN374101834<p>I'm trying out the new VS 2017 RC and wondering if anyone knows how to get the previous debugging behavior back</p>\n\n<p>In VS 2015 it went like this:</p>\n\n<p>Press start debugging</p>\n\n<ul>\n<li>Website opens in new Chrome tab</li>\n<li>Press stop debugging</li>\n<li>Website is still open and the site is still running/active</li>\n</ul>\n\n<p>Now in 2017:</p>\n\n<ul>\n<li>Press start debugging</li>\n<li>Website opens in new window that can't dock with any other Chrome windows/tabs</li>\n<li>Press stop debugging</li>\n<li>Website/Chrome window closes, can't continue using the site unless I manually go to the localhost window in Chrome</li>\n</ul>\n\n<p>Is it possible in 2017 to switch back to the 2015 style? So the Chrome/Website window can dock with other Chrome windows/tabs, and it stays open after you stop debugging?</p>\n\n<p>Additionally, I find the new Chrome window frustrating to use, as it seems not to have any history/content available. E.g I can't autocomplete forms or urls, which is very annoying when I'm trying to test a form</p>\n290328.0NaN290328.0NaN2016-11-22 03:00:332019-05-03 19:15:37How to stop browser closing automatically when you stop debugging on VS 2017<visual-studio><visual-studio-2017>6081NaNNaNCC BY-SA 3.0
1193021194934126815894.0NaN2014-01-17 20:08:41NaN804576258<p>Is this the right way to create a directory if it doesn't exist.\nIt should have full permission for the script and readable by others.</p>\n\n<pre><code>var dir = __dirname + '/upload';\nif (!path.existsSync(dir)) {\n fs.mkdirSync(dir, 0744);\n}\n</code></pre>\n356380.0NaN7080548.0NaN2019-10-29 14:40:122020-12-03 17:43:31How to create a directory if it doesn't exist using Node.js?<node.js>19298NaNNaNCC BY-SA 4.0
1193148910876151024493.0NaN2018-02-21 16:30:30NaN449387510<p>What might be causing the error <code>Error: EACCES: permission denied, access '/usr/local/lib/node_modules'</code>?</p>\n\n<pre><code>npm ERR! path /usr/local/lib/node_modules\nnpm ERR! code EACCES\nnpm ERR! errno -13\nnpm ERR! syscall access\nnpm ERR! Error: EACCES: permission denied, access '/usr/local/lib/node_modules'\nnpm ERR! { Error: EACCES: permission denied, access '/usr/local/lib/node_modules'\nnpm ERR! errno: -13,\nnpm ERR! code: 'EACCES',\nnpm ERR! syscall: 'access',\nnpm ERR! path: '/usr/local/lib/node_modules' }\nnpm ERR! \nnpm ERR! Please try running this command again as root/Administrator.\n\nnpm ERR! A complete log of this run can be found in:\nnpm ERR! /Users/macbookmd101/.npm/_logs/2018-02-21T16_26_08_421Z-debug.log\n</code></pre>\n9322906.0NaN2745495.0NaN2020-08-24 09:40:322021-02-19 14:13:57Error: EACCES: permission denied, access '/usr/local/lib/node_modules'<node.js><npm><permission-denied>341120NaNNaNCC BY-SA 4.0
1193213813254113813255.0NaN2012-12-11 03:39:53NaN313539380<p>Q1. Suppose I want to alter the look of each "item" that a user marks for deletion before the main "delete" button is pressed. (This immediate visual feedback should eliminate the need for the proverbial "are you sure?" dialog box.) The user will check checkboxes to indicate which items should be deleted. If a checkbox is unchecked, that item should revert back to its normal look.</p>\n\n<p>What's the best way to apply or remove the CSS styling?</p>\n\n<p>Q2. Suppose I want to allow each user to personalize how my site is presented. E.g., select from a fixed set of font sizes, allow user-definable foreground and background colors, etc.</p>\n\n<p>What's the best way to apply the CSS styling the user selects/inputs?</p>\n215945.0NaNNaNNaNNaN2019-02-08 15:14:05How do I conditionally apply CSS styles in AngularJS?<css><angularjs>141160NaNNaNCC BY-SA 3.0
1193333022662133023788.0NaN2015-10-08 17:48:07NaN16066470<p>I want to build an app that centers around getting the user's current location and then find points of interest(such as bars,restaurants,etc) that are close to him/her via the <strong>Google Places API</strong>. </p>\n\n<p>Upon searching the web for a place to start I came across some tutorials that use the <code>LocationManager</code> class and some others that use <strong>Google Play Services</strong> in order to find the users location.</p>\n\n<p>On first sight both of them do the same thing, but since I am new to this I got a little confused and I don't know which method suits my needs the best. So, I want to ask you :</p>\n\n<p><strong>What are the differences between these two methods of finding locations (if there are any) ?</strong></p>\n2740158.0NaNNaNNaNNaN2021-03-09 14:01:16Android : LocationManager vs Google Play Services<android><gps><geolocation>8184NaNNaNCC BY-SA 3.0
1193440823315140824044.0NaN2016-11-26 21:24:32NaN23777048<p>There are already several Q&amp;As on this "<em>X does not implement Y (... method has a pointer receiver)</em>" thing, but to me, they seems to be talking about different things, and not applying to my specific case.</p>\n\n<p>So, instead of making the question very specific, I'm making it broad and abstract -- Seems like there are several different cases that can make this error happen, can someone summary it up please?</p>\n\n<p>I.e., how to avoid the problem, and if it occurs, what are the possibilities? Thx.</p>\n2125837.0NaN13860.0NaN2018-09-10 16:40:082021-03-19 08:26:45X does not implement Y (... method has a pointer receiver)<pointers><go><methods><interface>4081NaNNaNCC BY-SA 4.0